ENGR 209A: Analysis and Control of Nonlinear Systems

Introduction to nonlinear phenomena: multiple equilibria, limit cycles, bifurcations, complex dynamical behavior. Planar dynamical systems, analysis using phase plane techniques. Describing functions. Lyapunov stability theory. SISO feedback linearization, sliding mode control. Design examples. Prerequisite: 205.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Rock, S. (PI)

ENGR 205: Introduction to Control Design Techniques

Review of root-locus and frequency response techniques for control system analysis and synthesis. State-space techniques for modeling, full-state feedback regulator design, pole placement, and observer design. Combined observer and regulator design. Lab experiments on computers connected to mechanical systems. Prerequisites: 105, MATH 103, 113. Recommended: Matlab.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Rock, S. (PI)

ARTSTUDI 260: Design II: The Bridge

The historical spectrum of design including practical and ritual. The values and conceptual orientation of visual fundamentals. Two- and three-dimensional projects grouped to relate design theory to application, balancing imaginative and responsible thinking. Prerequisite: ARTSTUDI 160. Corequisite: ME 203 (upper level). May be repeated for credit
Terms: Win, Spr | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Edmark, J. (PI)

ARTSTUDI 160: Design I : Fundamental Visual Language

Formal elements of visual expression (color, composition, space, and process) through hands-on projects. Two- and three-dimensional media. Emphasis is on originality and inventiveness. Content is realized abstractly. Centered in design; relevant to visual art study and any student seeking to develop visual perception. (lower level)
Terms: Aut, Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Edmark, J. (PI)

SOC 257: Causal Inference in Quantitative Educational and Social Science Research (EDUC 255B)

Quantitative methods to make causal inferences in the absence of randomized experiment including the use of natural and quasi-experiments, instrumental variables, regression discontinuity, matching estimators, longitudinal methods, fixed effects estimators, and selection modeling. Assumptions implicit in these approaches, and appropriateness in research situations. Students develop research proposals relying on these methods. Prerequisites: exposure to quantitative research methods; multivariate regression.
Terms: Win | Units: 3-5 | Grading: Letter (ABCD/NP)
Instructors: Dee, T. (PI)

SBIO 228: Computational Structural Biology (BIOPHYS 228)

Interatomic forces and interactions such as electrostatics and hydrophobicity, and protein structure in terms of amino acid properties, local chain conformation, secondary structure, domains, and families of folds. How protein motion can be simulated. Bioinformatics introduced in terms of methods that compare proteins via their amino acid sequences and their three-dimensional structures. Structure prediction via simple comparative modeling. How to detect and model remote homologues. Predicting the structure of a protein from knowledge of its amino acid sequence. Via Internet.
Terms: not given this year | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 104Q: Law and the Biosciences

Preference to sophomores. Focus is on human genetics; also assisted reproduction and neuroscience. Topics include forensic use of DNA, genetic testing, genetic discrimination, eugenics, cloning, pre-implantation genetic diagnosis, neuroscientific methods of lie detection, and genetic or neuroscience enhancement. Student presentations on research paper conclusions.
Terms: Win | Units: 3 | UG Reqs: Writing2 | Grading: Letter or Credit/No Credit
Instructors: Greely, H. (PI)

GENE 199: Undergraduate Research

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Letter or Credit/No Credit

GENE 200: Genetics and Developmental Biology Training Camp (DBIO 200)

Open to first year Department of Genetics and Developmental Biology students, to others with consent of instructors. Introduction to basic manipulations, both experimental and conceptual, in genetics and developmental biology.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Fire, A. (PI); Greenleaf, W. (PI); Kingsley, D. (PI); Li, J. (PI); Montgomery, S. (PI); Vollrath, D. (PI); Winslow, M. (PI)

GENE 202: Human Genetics

Utilizes lectures and small group discussions to develop a working knowlege of human genetics as applicable to clinical medicine and research. Basic principles of inheritance, risk assessment, and population genetics, illustrated by using clinical examples drawn from diverse areas of medical genetics practice including prenatal, pediatric, adult and cancer genetics. Practical aspects of molecular and cytogenetic diagnostic methods emphasized. Existing and emerging treatment strategies for single gene disorders also covered. Prerequisites: biochemistry; basic genetics.
Terms: Aut | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Bernstein, J. (PI)

GENE 206: Epigenetics (BIO 156, BIO 256, PATH 206)

For graduate students in the Biosciences and upper level Biology undergraduates. Mechanisms by which phenotypes not determined by the DNA sequence are stably inherited in successive cell divisions. From the discovery of position-effect variegation in Drosophila in the 1920s to present-day studies of covalent modifications of histones and DNA methylation. Topics include: position effect, gene silencing, heterochromatin, centromere identity, genomic imprinting, histone code, variant histones, and the role of epigenetics in cancer. Prerequisite: BIO41 and BIO42 , or GENE 203, or consent of instructor.
Terms: alternate years, given next year | Units: 2 | Grading: Letter or Credit/No Credit

GENE 209: Current Topics in Human, Population, and Statistical Genomics

Intensive seminar/workshop. Topics, drawn from current and past literature, may include: assessing and population genetic analysis of genomic variation; genome-to-phenome mapping; reconstructing demographic history from genome sequence data; domestication genomics; host-pathogen genome evolution; detecting signatures of selection; experimental design in human genetics; linkage and association mapping; ethical and social issues in human, plant, and animal genetics research. Emphasis on analysis and logic or experimental and observational genomics research. Faculty-led discussion with evaluation of response papers, problem sets, and intensive course project. May be repeated for credit.
Terms: Spr | Units: 2 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Bustamante, C. (PI)

GENE 210: Genomics and Personalized Medicine (DBIO 220)

Principles of genetics underlying associations between genetic variants and disease susceptibility and drug response. Topics include: genetic and environmental risk factors for complex genetic disorders; design and interpretation of genome-wide association studies; pharmacogenetics; full genome sequencing for disease gene discovery; population structure and genetic ancestry; use of personal genetic information in clinical medicine; ethical, legal, and social issues with personal genetic testing. Hands-on workshop making use of personal or publicly available genetic data. Prerequisite: GENE 202, Gene 203 or BIOS 200.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Gitler, A. (PI); Kim, S. (PI)

GENE 211: Genomics

Genome evolution, organization, and function; technical, computational, and experimental approaches; hands-on experience with representative computational tools used in genome science; and a beginning working knowledge of PERL.
Terms: Win | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 212: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, CS 272)

Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Prerequisites: BIOMEDIN 210, 211, 214, 217 or consent of instructor.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, CS 274)

Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, Gibbs Sampling, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisites: programming skills; consent of instructor for 3 units.
Terms: Aut | Units: 3-4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Altman, R. (PI)

GENE 215: Frontiers in Biological Research (BIOC 215, DBIO 215)

Literature discussion in conjunction with the Frontiers in Biological Research seminar series in which investigators present current work. Students and faculty meet beforehand to discuss papers from the speaker's primary research literature. Students meet with the speaker after the seminar to discuss their research and future direction, commonly used techniques to study problems in biology, and comparison between the genetic and biochemical approaches in biological research.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

GENE 218: Computational Analysis of Biological Information: Introduction to Python for Biologists (MI 218, PATH 218)

Physical and computational tools for acquisition, processing, interpretation, and archiving of biological images. Emphasis is on digital microscopy. Intended for biological and clinical trainees without substantial programming experience.
Terms: not given this year | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 221: Current Issues in Aging (DBIO 221)

Current research literature on genetic mechanisms of aging in animals and human beings. Topics include: mitochondria mutations, insulin-like signaling, sirtuins, aging in flies and worms, stem cells, human progeria, and centenarian studies. Prerequisite: GENE 203 or BIOS 200.
Terms: Spr | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Brunet, A. (PI)

GENE 224: Principles of Pharmacogenomics (BIOMEDIN 224)

Introduction to the relevant pharmacology, genomics, experimental methods for high-throughput measurements (sequencing, expression, genotyping), analysis methods for GWAS, chemoinformatics, and natural language processing. Review of key gene classes (cytochromes, transporters, GPCRs), key drugs for which genetics is critical (warfarin, clopidogrel, statins, NSAIDs, neuropsychiatric drugs and cancer drugs). Also reviews resources for pharmacogenomics (PharmGKB, Drugbank, CMAP, and others) as well as issues in doing clinical implementation of pharmacogenomics testing. Reading of key papers, including student presentations of this work.; problem sets; final project selected with approval of instructor. Prerequisites: two of BIO 41, 42, 43, 44X, 44Y or consent of instructor.
Terms: Win | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 233: The Biology of Small Modulatory RNAs (MI 233, PATH 233)

Open to graduate and medical students. Explores recent progress and unsolved questions in the field of RNA intereference and microRNA biology. Students are required to read assigned primary literature before each class and actively participate in guided discussions on related technical and conceptual issues during class meetings. Assignments include critiques of assigned papers and developing a novel research proposal.
Terms: not given this year | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 234: Fundamentals of RNA Biology (MI 234, PATH 234)

For graduate or medical students and (if space allows) to active participants from other segments of the Stanford Community (e.g., TGR students); undergraduates by instructor consent. Fundamental issues of RNA biology, with the goal of setting a foundation for students to explore the expanding world of RNA-based regulation. Each week a topic is covered by a faculty lecture and journal club presentations by students.
Terms: alternate years, given next year | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 235: C. Elegans Genetics

Genetic approaches to C. elegans, practice in designing experiments and demonstrations of its growth and anatomy. Probable topics include: growth and genetics, genome map and sequence, mutant screens that start with a desired phenotype, reverse genetics and RNAi screens, genetic duplications, uses of null phenotype non-null alleles, genetic interactions and pathway analysis, and embryogenesis and cell lineage. Focus of action, mosaic analysis, and interface with embryological and evolutionary approaches.
Terms: not given this year | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 243: Intellectual Propery: Scientific Evidence in Patent Litigation

(Same as LAW 343) Open to clinical MD and graduate students. Explores the role of scientific experts in patent infringement litigation. In other areas of the law where scientific experts are used -- medical malpractice, environmental law, criminal law -- the science itself is often in dispute. In patent cases, however, the parties generally agree on the science. This affects the relationship between the lawyer and the expert and the substantive content of their interactions. Patent experts need to be able to explain science to the judge and jury. But they also must help the litigators choose which legal issues to press and which to concede, and to be aware of how the complications of the science might help, hurt, obscure or reveal how the law should be applied to the facts. The class examines judicial decisions and trial documents involving scientific evidence in patent litigation, followed by work in teams on final projects: simulations of expert testimony in a patent case. Simulations are performed at the end of the quarter before panels of practicing patent lawyers. Prerequisite: graduate students must have completed their required coursework and have TGR status.
Terms: Spr | Units: 3 | Grading: Medical Satisfactory/No Credit
Instructors: Morris, R. (PI)

GENE 244: Introduction to Statistical Genetics

Statistical methods for analyzing human genetics studies of Mendelian disorders and common complex traits. Probable topics include: principles of population genetics; epidemiologic designs; familial aggregation; segregation analysis; linkage analysis; linkage-disequilibrium-based association mapping approaches; and genome-wide analysis based on high-throughput genotyping platforms. Prerequisite: STATS 116 or equivalent or consent of instructor.
Terms: alternate years, given next year | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 245: Computational Algorithms for Statistical Genetics (STATS 166, STATS 345)

Computational algorithms for human genetics research. Topics include: permutation, bootstrap, expectation maximization, hidden Markov model, and Markov chain Monte Carlo. Rationales and techniques illustrated with existing implementations commonly used in population genetics research, disease association studies, and genomics analysis. Prerequisite: GENE 244 or consent of instructor.
Terms: Spr, not given next year | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 260: Supervised Study

Genetics graduate student lab research from first quarter to filing of candidacy. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Satisfactory/No Credit

GENE 271: Human Molecular Genetics

For genetic counseling students, graduate students in genetics, medical students, residents, and postdoctoral fellows interested in the practice of medical genetics. Gene structure and function; the impact of mutation and polymorphism as they relate to developmental pathways and health and human disease; population based genetics; approaches to the study of complex genetic conditions; GWAS and genome sequencing technologies; variant curation; gene therapy, proteomics, stem cell biology, and pharmacogenetics. Undergraduates require consent of instructor and a basic genetics course.
Terms: Aut | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Kwan, A. (PI)

GENE 272: Introduction to Medical Genetics

For genetic counseling students, graduate students in human genetics, medical students, residents, and fellows; undergraduates with consent of instructor. Principles of medical genetics including taking a family history, modes of inheritance, and mathematical principles of medical genetics (Bayes theorem, population genetics). An additional problem set is required for 3 units.
Terms: Aut | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 273: Introduction to Clinical Genetics Testing

For genetic counseling students, medical students, residents, and fellows. Uses a combination of case based assignments and online didactic lectures to introduce the laboratory concepts of cytogenetics, molecular genetics and biochemical genetics, and to illustrate common genetic conditions that are diagnosed through such testing, introducing students to skills in case preparation, management and presentation.
Terms: Aut, Spr, Sum | Units: 1 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 274A: A Case Based Approach to Clinical Genetics

For genetic counseling students, graduate students in genetics, medical students, residents and fellows. Case-based scenarios and guest expert lectures. Students learn skills in case preparation, management, and presentation, as well as content around common genetic disorders.
Terms: Win | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 274B: A Case Based Approach to Clinical Genetics

For genetic counseling students, graduate students in genetics, medical students, residents, and fellows. Case-based scenarios and guest expert lectures. Students learn skills in case preparation, management, and presentation, as well as content around common genetic disorders.
Terms: Spr | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 275: Role Play and Genetic Counseling Observations

Students role play aspects of genetic counseling sessions and learn through clinical observations. Observation includes genetic counseling sessions in prenatal, pediatric, and cancer settings.
Terms: Aut | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Kwan, A. (PI); Chen, K. (SI); Wetzel, H. (SI)

GENE 276: Genetic Counseling Clinical Rotations

For genetic counseling students only. Supervised clinical experiences. May be repeated for credit. Prerequisite: GENE 275.
Terms: Aut, Win, Spr, Sum | Units: 4-7 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 278: Prenatal Genetic Counseling

Internet-based course for genetic counseling students, graduate students in genetics, medical students, residents, and fellows; genetic counseling students should take this course in conjunction with their initial prenatal genetics rotation. Topics include prenatal genetic screening and diagnosis in the first and second trimesters, ultrasound, teratology, and genetic carrier screening.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Medical Satisfactory/No Credit
Instructors: Ormond, K. (PI)

GENE 279: Pediatric and Adult Genetic Counseling

Internet-based course for genetic counseling students, graduate students in genetics, medical students, residents, and fellows; genetic counseling students should take this course in conjunction with their initial general genetics rotation. Topics include: common genetic conditions; assessment of child development and medical history in the context of a genetic workup; dysmorphology; development of a differential diagnosis; and resources for case management and family support.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Medical Satisfactory/No Credit

GENE 280: Metabolic Genetic Counseling

Internet-based course for genetic counseling students, graduate students in genetics, medical students, residents, and fellows; genetic counseling students should take this course in conjunction with their metabolic genetics rotation. Topics include: overview of metabolic diseases; common pathways; diagnosis, management, and treatment of metabolic disorders; and newborn screening.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Medical Satisfactory/No Credit

GENE 281: Cancer Genetic Counseling

Internet-based course for genetic counseling students, graduate students in genetics, medical students, residents, and fellows; genetic counseling students should take this course in conjunction with their initial cancer genetics rotation. Topics include: cancer biology and cytogenetics; diagnosis and management of common cancer genetic syndromes; predictive testing; psychology of cancer genetic counseling; and topics recommended by ASCO guidelines.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Medical Satisfactory/No Credit
Instructors: Ormond, K. (PI)

GENE 282A: Genetic Counseling Research Seminar

For genetic counseling students only. Facilitated discussions on identifying a topic and mentor for genetic counseling departmental research projects.
Terms: Win | Units: 1 | Grading: Letter or Credit/No Credit
Instructors: Ormond, K. (PI)

GENE 282B: Genetic Counseling Research Seminar

For genetic counseling students only. Lectures and facilitated discussions on research methodology for genetic counseling departmental research projects. Prerequisite: GENE 282A,
Terms: Spr | Units: 1 | Grading: Letter or Credit/No Credit
Instructors: Ormond, K. (PI)

GENE 283: Genetic Counseling Research

Genetic counseling students conduct clinical research projects as required by the department for graduation. May be repeated for credit. Pre- or corequisite:GENE 282.
Terms: Aut, Win, Spr, Sum | Units: 1-8 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)

GENE 284: Medical Genetics Seminar

Presentation of research and cases. Students enrolling for 2 units also attend and report on external seminars. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-2 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Kwan, A. (PI)

GENE 285A: Genetic Counseling Seminar

Year-long seminar primarily for genetic counseling students. Autumn: basics of medical communication; crosscultural and disability sensitive communication about genetics, and principles of providing genetic counseling. Undergraduates may enroll in Autumn Quarter with consent of instructor.
Terms: Aut | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Ormond, K. (PI)

GENE 285B: Genetics Counseling Seminar

Year-long seminar primarily for genetic counseling students. Winter: the impact of chronic illness and genetic disease in a developmental manner.
Terms: Win | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Ormond, K. (PI)

GENE 285C: Genetic Counseling Seminar

Year-long seminar primarily for genetic counseling students. Spring: applying therapeutic counseling approaches to the practice of genetic counseling.
Terms: Spr | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Ormond, K. (PI)

GENE 286: Advanced Genetic Counseling Seminar

For genetic counseling students only. Psychosocial issues associated with genetic counseling cases are discussed through presentation of cases that students have seen throughout their training. Professional development topics including: the expanding roles of genetic counselors; billing, reimbursement, and licensing; the role of genetic counseling in the changing healthcare system; the incorporation of genetics into all areas of medicine and public health; and implications of direct-to-consumer genetic testing. Must be taken for 3 quarters. Prerequisites: GENE 285 A,B,C and 276.
Terms: Aut, Win, Spr | Units: 2 | Repeatable for credit | Grading: Letter or Credit/No Credit

GENE 299: Directed Reading in Genetics

Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

GENE 399: Graduate Research

Investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

GENE 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

GENE 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

BIOC 109A: The Human Genome and Disease (BIO 109A, BIO 209A, BIOC 209A, HUMBIO 158)

The variability of the human genome and the role of genomic information in research, drug discovery, and human health. Concepts and interpretations of genomic markers in medical research and real life applications. Human genomes in diverse populations. Original contributions from thought leaders in academia and industry and interaction between students and guest lecturers. Students with a major, minor or coterm in Biology: 109A/209A or 109B/209B may count toward degree program but not both.
Terms: Win | Units: 3 | UG Reqs: GER:DBNatSci | Grading: Letter or Credit/No Credit
Instructors: Berg, P. (PI); Davis, R. (PI); Heller, R. (PI)

BIOC 109B: The Human Genome and Disease: Genetic Diversity and Personalized Medicine (BIO 109B, BIO 209B, BIOC 209B)

Continuation of 109A/209A. Genetic drift: the path of human predecessors out of Africa to Europe and then either through Asia to Australia or through northern Russia to Alaska down to the W. Coast of the Americas. Support for this idea through the histocompatibility genes and genetic sequences that predispose people to diseases. Guest lectures from academia and pharmaceutical companies. Prerequisite: Biology or Human Biology core. Students with a major, minor or coterm in Biology: 109A/209A or 109B/209B may count toward degree program but not both.
Terms: Spr | Units: 3 | UG Reqs: GER:DBNatSci | Grading: Letter or Credit/No Credit

BIOC 118Q: Genomics and Medicine

Preference to sophomores. Knowledge gained from sequencing human genomes and implications for medicine and biomedical research. Novel diagnoses and treatment of diseases, including stem cells, gene therapy and rational drug design. Personal genomics and how it is used to improve health and well being. Social and ethical implications of genetic information such as privacy, discrimination and insurability. Course Webpage: http://biochem118.stanford.edu/.
Terms: Aut | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Brutlag, D. (PI)

BIOC 158: Genomics, Bioinformatics and Medicine (BIOC 258, BIOMEDIN 258, HUMBIO 158G)

Molecular basis of inherited human disease. Diagnostics approaches: simple Mendelian diseases and complex, multifactorial diseases. Genomics: functional genomics, epigenetics, gene expression, SNPs, copy number and other structural genomic variations involved in disease. Novel therapeutic methods: stem cell therapy, gene therapy and drug developments that depend on the knowledge of genomics. Personal genomics, pharmacogenomics, clinical genomics and their role in the future of preventive medicine. Prerequisites: BIO 41 or HUMBIO 2A or consent of instructor. Those with credit in BIOC 118 not eligible to enroll. Course webpage: http://biochem158.stanford.edu/
Terms: Aut, Win, Spr | Units: 3 | UG Reqs: GER:DBNatSci | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Brutlag, D. (PI)

BIOC 199: Undergraduate Research

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Letter or Credit/No Credit

BIOC 200: Applied Biochemistry

Enrollment limited to MD candidates. Fundamental concepts of biochemistry as applied to clinical medicine. Topics include thermodynamics, enzyme kinetics, vitamins and cofactors, metabolism of carbohydrates, lipids, amino acids and nucleotides, and the integration of metabolic pathways. Clinical case studies discussed in small-group, problem-based learning sessions.
Terms: Aut | Units: 1 | Grading: Medical School MD Grades
Instructors: Cowan, T. (PI); Harbury, P. (PI); Theriot, J. (PI); Binkley, M. (TA); Jaju, P. (TA); Sloan, S. (TA); Tran, A. (TA)

BIOC 202: Biochemistry Bootcamp

Open to first year Biochemistry students or consent of instructor. Hands-on, five-day immersion in biochemical methods and practice, theory and application of light microscopy, and computational approaches to modern biological problems.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit

BIOC 205: Molecular Foundations of Medicine

For medical students. Topics include DNA structure, replication, repair, and recombination; gene expression, including mechanisms for regulating transcription and translation; chromosome structure and function; gene cloning, protein engineering, and genomics. Patient presentations and journal clubs illustrate how molecular biology affects the practice of medicine.
Terms: Aut | Units: 3 | Grading: Medical School MD Grades
Instructors: Chu, G. (PI); Krasnow, M. (PI); Beel, A. (TA); Brett, J. (TA)

BIOC 209A: The Human Genome and Disease (BIO 109A, BIO 209A, BIOC 109A, HUMBIO 158)

The variability of the human genome and the role of genomic information in research, drug discovery, and human health. Concepts and interpretations of genomic markers in medical research and real life applications. Human genomes in diverse populations. Original contributions from thought leaders in academia and industry and interaction between students and guest lecturers. Students with a major, minor or coterm in Biology: 109A/209A or 109B/209B may count toward degree program but not both.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Berg, P. (PI); Davis, R. (PI); Heller, R. (PI)

BIOC 209B: The Human Genome and Disease: Genetic Diversity and Personalized Medicine (BIO 109B, BIO 209B, BIOC 109B)

Continuation of 109A/209A. Genetic drift: the path of human predecessors out of Africa to Europe and then either through Asia to Australia or through northern Russia to Alaska down to the W. Coast of the Americas. Support for this idea through the histocompatibility genes and genetic sequences that predispose people to diseases. Guest lectures from academia and pharmaceutical companies. Prerequisite: Biology or Human Biology core. Students with a major, minor or coterm in Biology: 109A/209A or 109B/209B may count toward degree program but not both.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

BIOC 210: Advanced Topics in Membrane Trafficking

The structure, function, and biosynthesis of cellular membranes and organelles. Current literature. Prerequisite: consent of instructor.
Terms: not given this year | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOC 215: Frontiers in Biological Research (DBIO 215, GENE 215)

Literature discussion in conjunction with the Frontiers in Biological Research seminar series in which investigators present current work. Students and faculty meet beforehand to discuss papers from the speaker's primary research literature. Students meet with the speaker after the seminar to discuss their research and future direction, commonly used techniques to study problems in biology, and comparison between the genetic and biochemical approaches in biological research.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIOC 218: Computational Molecular Biology (BIOMEDIN 231)

Practical, hands-on approach to field of computational molecular biology. Recommended for molecular biologists and computer scientists desiring to understand the major issues concerning analysis of genomes, sequences and structures. Various existing methods critically described and strengths and limitations of each. Practical assignments utilizing tools described. Prerequisite: BIO 41 or consent of instructor. All homework and coursework submitted electronically. Course webpage: http://biochem218.stanford.edu/.
Terms: Aut, Win, Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Brutlag, D. (PI)

BIOC 220: Chemistry of Biological Processes (CSB 220)

The principles of organic and physical chemistry as applied to biomolecules. Goal is a working knowledge of chemical principles that underlie biological processes, and chemical tools used to study and manipulate biological systems. Prerequisites: organic chemistry and biochemistry, or consent of instructor.
Terms: Spr | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOC 221: The Teaching of Biochemistry

Required for teaching assistants in Biochemistry. Practical experience in teaching on a one-to-one basis, and problem set design and analysis. Familiarization with current lecture and text materials; evaluations of class papers and examinations. Prerequisite: enrollment in the Biochemistry Ph.D. program or consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 3 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIOC 224: Advanced Cell Biology (BIO 214, MCP 221)

For Ph.D. students. Current research on cell structure, function, and dynamics. Topics include complex cell phenomena such as cell division, apoptosis, compartmentalization, transport and trafficking, motility and adhesion, differentiation, and multicellularity. Current papers from the primary literature. Prerequisite for advanced undergraduates: BIO 129A,B, and consent of instructor.
Terms: Win | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Jonikas, M. (PI); Kopito, R. (PI); Pfeffer, S. (PI); Theriot, J. (PI)

BIOC 226: Interdisciplinary Approaches to Biochemistry: Single Molecule Biophysics to Clinical Outcomes

Interdisciplinary analyses from basic biochemistry and biophysics to clinical outcomes of disease states and potential therapeutic interventions (translational research). Focus on cardiac system. Cardiomyopathies arise from missense mutations in cardiac muscle proteins, including the cardiac myosin motor. Single molecule biophysics and classical enzyme kinetics and use of induced pluripotent stem cells (iPS cells) and single cell studies lay foundation for discussions of effects of cardiomyopathy mutations on heart function. Potential therapeutic approaches discussed, including genetic analysis, DNA cloning, reconstitution of functional assemblies, x-ray diffraction and 3D reconstruction of electron microscope images, spectroscopic methods, computational approaches, single molecule biophysics, use of induced pluripotent stem cells in research, and other interdisciplinary approaches. Current papers examined. Prerequisites: basic biochemistry.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

BIOC 236: Biology by the Numbers: Evolution (APPPHYS 236)

Topics in biology from a quantitative perspective. Subjects vary. 2012-13 focus: evolution, from basic principles of evolutionary dynamics to fundamental quantitative questions that are far from being answered; from early life, metabolic processes, and molding of earth by microbes to spread of human epidemics; from analysis of genomes and molecular phylogenies to aspects of multi-cellular development. Prerequisite: familiarity with ordinary differential equations and probability. Biology background not required.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fisher, D. (PI)

BIOC 241: Biological Macromolecules (BIOPHYS 241, SBIO 241)

The physical and chemical basis of macromolecular function. Forces that stabilize biopolymers with three-dimensional structures and their functional implications. Thermodynamics, molecular forces, structure and kinetics of enzymatic and diffusional processes, and relationship to their practical application in experimental design and interpretation. Biological function and the level of individual molecular interactions and at the level of complex processes. Case studies in lecture and discussion of classic and current literature. Enrollment limited to 30. Prerequisites: None; background in biochemistry and physical chemistry preferred but material available for those with deficiency; undergraduates with consent of instructor only.
Terms: Spr | Units: 3-5 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOC 257: Currents in Biochemistry

Seminars by Biochemistry faculty on their ongoing research. Background, current advances and retreats, general significance, and tactical and strategic research directions.
Terms: Aut | Units: 1 | Grading: Medical Satisfactory/No Credit
Instructors: Spudich, J. (PI)

BIOC 258: Genomics, Bioinformatics and Medicine (BIOC 158, BIOMEDIN 258, HUMBIO 158G)

Molecular basis of inherited human disease. Diagnostics approaches: simple Mendelian diseases and complex, multifactorial diseases. Genomics: functional genomics, epigenetics, gene expression, SNPs, copy number and other structural genomic variations involved in disease. Novel therapeutic methods: stem cell therapy, gene therapy and drug developments that depend on the knowledge of genomics. Personal genomics, pharmacogenomics, clinical genomics and their role in the future of preventive medicine. Prerequisites: BIO 41 or HUMBIO 2A or consent of instructor. Those with credit in BIOC 118 not eligible to enroll. Course webpage: http://biochem158.stanford.edu/
Terms: Aut, Win, Spr | Units: 3 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Brutlag, D. (PI)

BIOC 299: Directed Reading in Biochemistry

Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIOC 350: Development of Thesis Research

Biochemistry 2nd year PhD students with permission of instructor only. Students place their thesis research into a broader scientific perspective, identify important questions to ask, and learn to communicate these clearly. Series of roundtable discussions with students and faculty about the proposed research topics. Initial focus on developing the equivalent of specific aims for research grants.
Terms: Aut | Units: 2 | Repeatable for credit | Grading: Satisfactory/No Credit

BIOC 360: Developing an Original Research Proposal

Biochemistry 3rd year PhD students with permission of instructor only. Students develop new research directions. Topics well outside of student's research topic must be chosen. Series of discussion groups with faculty and students. Students present possible outside research topics followed by presentation of important questions and approaches to answer these questions. Focus is on developing the equivalent of specific aims for research grants.
Terms: Aut, Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

BIOC 370: Medical Scholars Research

Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum | Units: 4-18 | Repeatable for credit | Grading: Medical School MD Grades

BIOC 399: Graduate Research and Special Advanced Work

Allows for qualified students to undertake investigations sponsored by individual faculty members.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIOC 459: Frontiers in Interdisciplinary Biosciences (BIO 459, BIOE 459, CHEM 459, CHEMENG 459, PSYCH 459)

Students register through their affiliated department; otherwise register for CHEMENG 459. For specialists and non-specialists. Sponsored by the Stanford BioX Program. Three seminars per quarter address scientific and technical themes related to interdisciplinary approaches in bioengineering, medicine, and the chemical, physical, and biological sciences. Leading investigators from Stanford and the world present breakthroughs and endeavors that cut across core disciplines. Pre-seminars introduce basic concepts and background for non-experts. Registered students attend all pre-seminars; others welcome. See http://biox.stanford.edu/courses/459.html. Recommended: basic mathematics, biology, chemistry, and physics.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Robertson, C. (PI)

BIOC 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

BIOC 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

PSYCH 1: Introduction to Psychology

Human behavior and mental processes including the nervous system, consciousness, learning, memory, development, emotion, psychopathology, interpersonal process, society, and culture. Current research.
Terms: Aut, Win, Spr | Units: 5 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 7Q: Language Understanding by Children and Adults

How do we first learn to find meaning in strings of speech sounds? Understanding spoken language requires the rapid integration of acoustic information with linguistic knowledge and with conceptual knowledge based on experience with how things happen in the world. Topics include research on early development of language understanding and laboratory methods of how young children make sense of speech. Observations of preschool children and visits to Stanford laboratories. Might be repeatable for credit.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Fernald, A. (PI)

PSYCH 8N: The New Longevity

Adult development from the perspective of life-span theory -- a conceptual framework that views development as a series of adaptations to physical, societal and individual resources and constraints. Students will learn about demographic and medical changes, ways that individuals typically change socially, emotionally and cognitively as they move through adulthood. An understanding of the conceptual foundations of the life-span approach and place aging of young people today in historical context.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)

PSYCH 10: Introduction to Statistical Methods: Precalculus (STATS 60, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Bacallado de Lara, S. (PI); Baiocchi, M. (PI); Richards, W. (PI); Thomas, E. (PI); Walther, G. (PI)

PSYCH 11N: Origin of Mental Life

Preference to freshmen. Mental life in infancy; how thinking originates. How do babies construe the objects, events, people, and language that surround them? Recent advances in psychological theory, hypotheses, and evidence about how the infant human mind develops.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 12N: Self Theories

Preference to freshmen. The impact of people's belief in a growing versus fixed self on their motivation and performance in school, business, sports, and relationships. How such theories develop and can be changed.
Terms: Aut | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Dweck, C. (PI)

PSYCH 13N: Emotion Regulation

This seminar provides a selective overview of the scientific study of emotion regulation. Topics include: theoretical foundations, cognitive consequences, developmental approaches, personality processes and individual differences, and clinical and treatment implications. Our focus is on interesting, experimentally tractable ideas. Meetings will be discussion based.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Gross, J. (PI)

PSYCH 13S: Dynamical models of mental processes: Development, analysis, and simulation

Mathematical modeling has been a critical component in modern psychological and cognitive neuroscience research on the dynamics of mental processes. This course is designed to equip the new generation of such scientists with tailored mathematical knowledge to develop models of their own. I will use classical models and my own experience in modeling decision making as examples to demonstrate the process from vague ideas to the development, refinement, analysis and simulation of dynamical models. Along the way, systematic knowledge in differential equations, numerical methods, principle component analysis etc will be provided to facilitate the general ground for future models of students¿ choosing. Open to graduate students and advanced undergraduates.
Terms: Sum | Units: 2 | Grading: Letter or Credit/No Credit

PSYCH 15N: Interpersonal Influence

This course will examine how individuals influence each other, both intentionally as well as nonconsciously. The focus will be on individuals in dyads rather than in groups. We will examine a) subtle interpersonal influence processes such as nonverbal communication, b) structural sources of interpersonal influence such as gender, race, social class, and culture, and c) interpersonal influence within different relationships such as organizational and romantic relationships. Familiarity with technology and video editing is useful. Students will have the opportunity to make brief podcasts and iMovie videos, as weekly responses to readings, as well as for the final class project.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Ambady, N. (PI)

PSYCH 17N: Language and Society: How Languages Shape Lives

Do people who speak different languages think differently? What role does language play in politics, law, and religion? The role of language in individual cognition and in society. Breaking news about language and society; the scientific basis for thinking about these broad issues.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 25N: Psychology, Inequality, and the American Dream

Despite legal prohibitions against discrimination and the fact that many people endorse egalitarian values, inequality persists in America. What role do psychological factors play in perpetuating inequality? How can psychologically "wise" reforms promote equal opportunity? Topics include prejudice and discrimination, school achievement, social class, and race/ethnicity.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 26N: Language Acquisition: Exploring the Minds of Children

Language is an extraordinary competence distinguishing humans from other species, yet there is debate about the role of biology in guiding language acquisition. Does language development follow an innate ¿bioprogram¿ or does it build on more general cognitive abilities, influenced by early experience? Topics include biological and experiential influences on the emergence of linguistic ability as children learn a first language. Discussions of theory and research, visits to Stanford laboratories and observations of very young language learners.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Fernald, A. (PI)

PSYCH 27N: The Psychology of Prejudice

Preference to freshmen. Social psychological theories and research on stereotypes, prejudice, discrimination, and racism. Psychological perspectives include those emphasizing personologic, cognitive, motivational, and sociocultural contributions to prejudice. Emphasis is on applying each approach to understanding real-world contexts such as educational and occupational contexts, and to the implications of this research for efforts to reduce prejudice and discrimination.
Terms: not given this year | Units: 3 | Grading: Satisfactory/No Credit

PSYCH 30: Introduction to Perception

Behavioral and neural aspects of perception focusing on visual and auditory perception. Topics include: scientific methods for studying perception, anatomy and physiology of the visual and auditiory systems, color vision, depth perception, motion perception, stereopsis, visual recognition, pitch and loudness perception, speech perception, and reorganization of the visual system in the blind.
Terms: Aut | Units: 3 | UG Reqs: GER:DBNatSci | Grading: Letter or Credit/No Credit
Instructors: Grill-Spector, K. (PI)

PSYCH 35: Introduction to Cognitive and Information Sciences (LINGUIST 144, PHIL 190, SYMSYS 100)

The history, foundations, and accomplishments of the cognitive sciences, including presentations by leading Stanford researchers in artificial intelligence, linguistics, philosophy, and psychology. Overview of the issues addressed in the Symbolic Systems major.
Terms: Win | Units: 4 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Goodman, N. (PI)

PSYCH 45: Introduction to Learning and Memory

The literature on learning and memory including cognitive and neural organization of memory, mechanisms of remembering and forgetting, and why people sometimes falsely remember events that never happened. Cognitive theory and behavioral evidence integrated with data from patient studies and functional brain imaging. Recommended: 1.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Wagner, A. (PI)

PSYCH 50: Introduction to Cognitive Neuroscience

Survey of topics relating brain activity to cognitive processes and behavior. The course begins with an overview of neurophysiology and techniques to measure brain activity. We then discuss perceptual and motor processes before investigating neural responses related to attention, memory, and cognitive control. The course concludes with a discussion of brain processes related to reward, decision making, and social cognition.
Terms: Aut | Units: 4 | UG Reqs: GER:DBNatSci | Grading: Letter or Credit/No Credit
Instructors: Uncapher, M. (PI)

PSYCH 55: Introduction to Cognition and the Brain

Major topics in cognitive psychology and neuroscience, including empirical approaches to perception, language, learning, memory, knowledge representation, problem solving, and reasoning.
Terms: not given this year | Units: 4 | Grading: Letter or Credit/No Credit

PSYCH 60: Introduction to Developmental Psychology

Psychological development from birth to adulthood, emphasizing infancy and the early and middle childhood years. The nature of change during childhood and theories of development. Recommended: 1.
Terms: Aut | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Frank, M. (PI)

PSYCH 60A: Introduction to Developmental Psychology Section

Guided observation of children age 2-5 at Bing Nursery School. Corequisite: 60.
Terms: Aut | Units: 2 | Grading: Letter or Credit/No Credit

PSYCH 70: Introduction to Social Psychology

Topics related to the influence of other people on individuals' thoughts, emotions, and behaviors. Factors that affect the way that we perceive ourselves and others; how people influence others; how persuasion happens; what causes us to like, love, help, or hurt others; and how social psychology helps to understand quesions about law, business, and health. Fulfills WIM requirement
Terms: Spr | Units: 4 | UG Reqs: GER:DBSocSci | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Ambady, N. (PI); Eberhardt, J. (PI)

PSYCH 75: Introduction to Cultural Psychology

The cultural sources of diversity in thinking, emotion, motivation, self, personality, morality, development, and psychopathology.
Terms: not given this year | Units: 5 | UG Reqs: GER:DBSocSci, GER:ECGlobalCom | Grading: Letter or Credit/No Credit

PSYCH 80: Introduction to Personality and Affective Science

How do we measure personality and emotion? What parts of your personality and emotions are set at birth? What parts of your personality and emotions are shaped by your sociocultural context? Can your personality and emotions make you sick? Can you change yours personality and emotions? There are questions we begin to address in this introductory course on personality and emotion. Prerequisite: 1.
Terms: Spr | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Tsai, J. (PI)

PSYCH 90: Introduction to Clinical Psychology

History of clinical psychology, models and assessment of personality, behavior, cognition, psychopathology, and approaches to the treatment of abnormal behavior. Emphasis is on current theory, research, issues in, and the role of clinical psychology in contemporary society. Recommended: 1.
Terms: Aut | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Gotlib, I. (PI)

PSYCH 95: Introduction to Abnormal Psychology

Theories of and approaches to understanding the phenomenology, etiology, and treatment of psychological disorders among adults and children. Research findings and diagnostic issues. Recommended: PSYCH 1.
Terms: Win | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 101: Community Health Psychology (HUMBIO 128)

Social ecological perspective on health emphasizing how individual health behavior is shaped by social forces. Topics include: biobehavioral factors in health; health behavior change; community health promotion; and psychological aspects of illness, patient care, and chronic disease management. Prerequisites: HUMBIO 3B or PSYCH 1, or equivalent.
Terms: Win | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Heaney, C. (PI)

PSYCH 102: Longevity (HUMBIO 149L, NENS 202)

Interdisciplinary. Challenges to and solutions for the young from increased human life expectancy: health care, financial markets, families, work, and politics. Guest lectures from engineers, economists, geneticists, and physiologists.
Terms: not given this year | Units: 4 | UG Reqs: GER:DBSocSci | Grading: Medical Option (Med-Ltr-CR/NC)

PSYCH 104: Uniquely Human

Are humans the only species that displays altruism, experiences uncertainty, and is capable of language and deception? Sources include empirical and theoretical papers in comparative psychology. Prerequisite: 1.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 104S: Introduction to Cognitive Neuroscience

Introduction to the neurobiology of mental processes and behaviors including the history of cognitive neuroscience, biology of nervous system, the neural basis for perception, attention, learning, memory, emotion, decision making and social behaviors. Introduction to different research techniques that are prevalent in current neuroscience studies including fMRI, EEG, TMS and single unit recording.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Rodriguez, C. (PI); Tian, M. (PI)

PSYCH 105: Social Neuroscience

Over the last 20 years, neuroscientists have become increasingly interested in studying topics that were previously the purview of social psychologists. In this seminar, we will survey neuroimaging research on topics such as self perception, person perception, empathy, and social influence. More broadly, we will consider the contributions that neuroscience can (and cannot) make to social psychological theory. Students will be responsible for leading discussions and producing one in-depth review or research paper at the end of the quarter.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Zaki, J. (PI)

PSYCH 105S: General Psychology

In what ways does the scientific study of psychology increase our understanding of the thoughts, feelings, and behaviors we observe and experience in everyday life? What are the main areas of psychology and the different questions they seek to answer? This course will give you an introduction to the field of psychology and its many different areas. You will learn about the central methods, findings, and unanswered questions of these areas, as well as how to interpret and critically evaluate research findings.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Szczurek, L. (PI)

PSYCH 106: Seminar on Visual Development

Describe basic development of visual system, introduce research methods/experimental designs, and present pathologies of visual development.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Farzin, F. (PI)

PSYCH 107: Visual Processing of Faces

How do we perceive a face, recognize its identity or judge its subtle communicative cues (e.g. emotion or intention)? How does our ability to visually process faces develop with age and change though out our life span? What is the role of nature vs. nurture in this development? How do social attitudes, culture and face perception interact? In addressing these questions, we will learn about behavioral, electrophysiological and neuroimaging approaches to understanding face processing and critically examine the theories and original research that have defined the field. The course is designed to give you an in depth understanding of face processing while exposing you to methods and ideas that are useful in evaluating a wide range of cognitive neuroscience research.
Terms: Win | Units: 2-3 | Grading: Letter or Credit/No Credit

PSYCH 107S: Introduction to Social Psychology

A comprehensive overview of social psychology with in-depth lectures exploring the history of the field, reviewing major findings and highlighting areas of current research. Focus is on classic studies that have profoundly changed our understanding of human nature and social interaction, and, in turn, have triggered significant paradigm shifts within the field. Topics include: individuals and groups, conformity and obedience, attraction, intergroup relations, and judgment and decision-making.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 108S: Social Psychology

This course attempts to blend a comprehensive overview of social psychology with in-depth lectures exploring the history of the field, reviewing major findings and highlighting areas of current research. The course will focus on classic studies that have profoundly changed our understanding of human nature and social interaction, and, in turn, have triggered significant paradigm shifts within the field. Some of the topics covered in the class will include: individuals and groups, conformity and obedience, attraction, intergroup relations, and judgment and decision-making.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fu, A. (PI); Okonofua, J. (PI); Zhang, S. (PI)

PSYCH 109S: Cognitive Psychology

How are you understanding this question as you read it? How are you perceiving these words? How do you remember what you just read? Together, we will discuss how these questions are addressed in the areas of memory, language, perception, reasoning, judgment and decision-making. This course will be divided into 3 sections, each devoted to one basic question of cognitive psychology: how do we perceive? How do we remember? How do we think? The goals of this course are to examine these questions and to introduce the theories and empirical findings in the field of cognitive psychology.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Henetz, T. (PI)

PSYCH 110: Research Methods and Experimental Design

Structured research exercises and design of an individual research project. Prerequisite: consent of instructor.
Terms: not given this year | Units: 5 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 111S: Abnormal Psychology

This course will provide an introduction to abnormal psychology and psychological treatments for mental illness. We will begin by exploring the basic question, ¿What does it mean to be abnormal exactly?¿ We will then go onto discuss various mental disorders, including depression, anxiety, obsessive compulsive personality disorder, and schizophrenia. While covering each disorder, we will pay particular attention to discussing how emotion ¿ and emotion regulation ¿ processes break down. In the second part of the course, we will discuss various psychological treatments for mental disorders, including cognitive behavioral therapy, interpersonal therapy, and more recent approaches such as cognitive bias modification and mindfulness meditation.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Thiruchselvam, R. (PI)

PSYCH 113S: Developmental Psychology

This class will introduce students to the basic principles of developmental psychology. As well as providing a more classic general overview, we will also look towards current methods and findings. Students will gain an appreciation of how developmental psychology as a science can be applied to their general understanding of children and the complicated process of growing into adults.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hoffmann Bion, R. (PI); Holubar, T. (PI); Horowitz, A. (PI)

PSYCH 115S: Personality Psychology

This course will focus on current empirical and theoretical approaches to personality. Lectures will be organized around the following questions central to personality research: How and why do people differ? How do we measure individual differences? Does personality change over time? How does personality interact with sociocultural factors to influence behavior? What makes people happy? What are the physical, mental, and social consequences of personalities?
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Chim, L. (PI); Koopmann-Holm, B. (PI); Notthoff, N. (PI)

PSYCH 118F: Literature and the Brain (ENGLISH 118, ENGLISH 218, FRENCH 118, FRENCH 318)

Recent developments in and neuroscience and experimental psychology have transformed the way we think about the operations of the brain. What can we learn from this about the nature and function of literary texts? Can innovative ways of speaking affect ways of thinking? Do creative metaphors draw on embodied cognition? Can fictions strengthen our "theory of mind" capabilities? What role does mental imagery play in the appreciation of descriptions? Does (weak) modularity help explain the mechanism and purpose of self-reflexivity? Can the distinctions among types of memory shed light on what narrative works have to offer?
Terms: Aut | Units: 5 | UG Reqs: GER:DBHum | Grading: Letter or Credit/No Credit

PSYCH 119: Psychology and Public Policy (PUBLPOL 172)

Applications of psychology to public and social policy. Factors that affect the influence of psychological research and individual psychology on the creation of policy, and the influence of policy on attitudes and behavior at the personal and societal levels. Topics include education, health care, and criminal justice.
Terms: not given this year | Units: 5 | Grading: Letter or Credit/No Credit

PSYCH 119S: The Psychology of Stigma

What obese people, African Americans, people with physical disabilities, lesbians, and Muslims have in common: social stigma. The social and psychological experiences of individuals living with social stigmas. Classic and current theory and research. Topics include: function, nature, and types of stigma; how stigmatized individuals view their identities and cope; mental and cognitive consequences; and interactions between stigmatized and non-stigmatized. Literature employing research methods including neuroimaging and social interaction studies.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 120: Cellular Neuroscience: Cell Signaling and Behavior (BIO 153)

Neural interactions underlying behavior. Prerequisites: PSYCH 1 or basic biology.
Terms: not given this year | Units: 4 | UG Reqs: GER:DBNatSci | Grading: Letter or Credit/No Credit

PSYCH 121: Ion Transport and Intracellular Messengers (PSYCH 228)

(Graduate students register for 228.) Ion channels, carriers, ion pumps, and their regulation by intracellular messengers in a variety of cell types. Recommended: 120, introductory course in biology or human biology.
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Wine, J. (PI)

PSYCH 124S: Applying Psychology to Modern Life

A scientific examination of everyday modern life. Topics include: how research on attention and memory can be applied to improve study strategies; how advertisers persuade and how their techniques can be resisted; how interpersonal conflicts can be avoided through knowledge of common errors in judging other people; and how studies on attraction and love can improve close relationships.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 125: Beyond Stereotype Threat: Claiming a Rightful Place in an Academic Community (CTL 130)

Stereotype threat as mitigating the quality of a student's test performance; its impact on academic success at Stanford. How to reduce the impact of stereotype threat on Stanford students.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 131: Language and Thought (PSYCH 262)

The psychology of language including: production and understanding in utterances; from speech sounds to speaker's meaning; children's acquisition of the first language; and the psychological basis for language systems. Language functions in natural contexts and their relation to the processes by which language is produced, understood, and acquired. Prerequisite: 1 or LINGUIST 1.
Terms: Aut | Units: 4 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Henetz, T. (PI)

PSYCH 134: Seminar on Language and Deception

Deceptive, exploitative, and other noncooperative uses of language. How is language used to deceive or exploit? Where are these techniques practiced and why? What are the personal, ethical, and social consequences of these practices? Prerequisite: 131, LINGUIST 1, or PHIL 181.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 138: Wise Interventions (PSYCH 238)

Classic and contemporary psychological interventions; the role of psychological factors in social reforms for social problems involving healthcare, the workplace, education, intergroup, relations, and the law. Topics include theories of intervention, the role of laboratory research, evaluation, and social policy.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Walton, G. (PI)

PSYCH 140S: Sport Psychology

Focus is on research methods and findings and how to apply these findings to students' own performance. Topics include methods of performance enhancement, psychological characteristics of top performers, group dynamics and processes, effective leadership practices, the effects of stereotyping on sport participation and performance, and debates in the field. Emphasis will be on sports, although most topics can be applied to performance in general.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 141: Cognitive Development

How children's thinking and mental abilities change from infancy on. The major theories and explanations of intellectual growth. Sources include classic findings and state-of-the-art research on cognitive development. Prerequisite: 1.
Terms: Aut | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Markman, E. (PI)

PSYCH 141S: Health Psychology

Why is it so difficult for people to stick to an exercise plan? Why don¿t people take their doctor¿s advice? Why aren¿t public health announcements more effective? This course addresses these questions by providing an overview of health psychology: the scientific study of behaviors and cognitive processes related to health states. In this course, we will discuss the mind/body connection, the influence of social/cultural and physical environments on our health, cognitive processing of health information, health belief models, and the link between emotion and health. Understanding the interactions between these biological, psychological, and social influences on individuals' health states is crucial for developing effective health communication and intervention programs. We will approach all course topics from both theory-driven and applied perspectives.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 142S: The Psychology of Social Media

People interact with the world around them largely through mediated means ¿ internet, television, radio, etc. This course will survey current social media ¿ e.g. Facebook, Twitter, YouTube, etc ¿ and popular culture in order to highlight the psychological processes at play. Topics will include: social belonging, interpersonal attraction, identity, bias, and cyberbullying. Students will be expected to learn how to study social media and popular culture using psychological methods.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 143: Developmental Anomalies

For advanced students. Developmental disorders and impairments. What the sparing of mental abilities in otherwise devastating disorders (or vice versa) tells about the mind and its development in the normal case. Examples of disorders and impairments: autism, congenital blindness, deafness, mental retardation, attachment disorder, and Williams syndrome. Limited enrollment. Prerequisite: consent of instructor.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 145: Seminar on Infant Development

For students preparing honors research. Conceptual and methodological issues related to research on developmental psycholinguistics; training in experimental design; and collection, analysis, and interpretation of data.
Terms: Spr | Units: 1-2 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Fernald, A. (PI)

PSYCH 146: Observation of Children

Learning about children through guided observations at Bing Nursery School, Psychology's lab for research and training in child development. Physical, emotional, social, cognitive, and language development. Recommended: 60.
Terms: Win, Spr | Units: 3-5 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit

PSYCH 147: Development in Early Childhood

Supervised experience with young children at Bing Nursery School. 3 units require 4 hours per week in Bing classrooms throughout the quarter; 4 units require 7 hours per week; 5 units require 10.5 hours per week. Seminar on developmental issues in the Bing teaching/learning environment. Recommended: 60 or 146, or consent of instructor.
Terms: Aut, Win, Spr | Units: 3-5 | Grading: Letter or Credit/No Credit

PSYCH 148: Introduction to Counseling (EDUC 130)

The goal of counseling is to help others to create more satisfying lives for themselves. Clients learn to create and capitalize on unexpected events to open up new opportunities. The success of counseling is judged, not by the words and actions of the counselor, but by the progress that the client makes in the real world after counseling itself is ended. Students are encouraged to exert their full efforts within reasonable time limits to improve their competence.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Krumboltz, J. (PI)

PSYCH 149: The Infant Mind: Cognitive Development over the First Year

How do babies learn so much in so little time? Emphasis is on cognitive and perceptual development, and the relationship between brain and behavior in infancy. Prerequisite: 1. Recommended: 60 or 141.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)

PSYCH 150: Race and Crime

The goal of this course is to examine social psychological perspectives on race, crime, and punishment in the United States. Readings will be drawn not only from psychology, but also from sociology, criminology, economics, and legal studies. We will consider the manner in which social psychological variables may operate at various points in the crimina justice system- from policing, to sentencing, to imprisonment, to re-entry. Conducted as a seminar.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 151: Emotion Regulation and Psychopathology

A broad overview of specific emotion regulation impairments in various psychopathologies and discussion of how current treatment protocols are likely to aid recovery by forming more adaptive emotion regulation ability. Topics include: Foundations and Emotion regulation models, Emotion regulation impairments in Mood disorders (Unipolar Depression and Bipolar Disorder), Anxiety disorders (Social Phobia, Post Traumatic Stress Disorder, General Anxiety Disorder), Eating disorders (Anorexia and Bulimia Nervosa), and Personality Disorders (Narcissistic Personality Disorder, Borderline Personality Disorder).
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 152: Mediation for Dispute Resolution (EDUC 131)

Mediation as more effective and less expensive than other forms of settling disputes such as violence, lawsuits, or arbitration. How mediation can be structured to maximize the chances for success. Simulated mediation sessions.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Krumboltz, J. (PI)

PSYCH 154: Judgment and Decision-Making

Survey of research on how we make assessments and decisions particularly in situations involving uncertainty. Emphasis will be on instances where behavior deviates from optimality. Overview of recent works examining the neural basis of judgment and decision-making.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 155: Introduction to Comparative Studies in Race and Ethnicity (ANTHRO 33, CSRE 196C, ENGLISH 172D, SOC 146)

How different disciplines approach topics and issues central to the study of ethnic and race relations in the U.S. and elsewhere. Lectures by senior faculty affiliated with CSRE. Discussions led by CSRE teaching fellows.
Terms: Win | Units: 5 | UG Reqs: GER:DBSocSci, GER:ECAmerCul | Grading: Letter or Credit/No Credit
Instructors: Brody, J. (PI); Fullwiley, D. (PI)

PSYCH 157: Social Foundations of Expertise and Intelligence

Psychological conceptions of expertise, ability, and intelligence and the research methods used to study these attributes. Topics include: research on how expertise in a diverse set of disciplines is developed; the role of practice in nurturing expertise; whether intelligence predicts life outcomes; the genetic and environmental determinants of intelligence; whether genes or environment explain racial differences such as the Black-White performance gap and the East Asian achievement advantage; and the Flynn effect.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 158: Emotions: History, Theories, and Research (PSYCH 259)

Graduate students register for 259. Theoretical and empirical issues in the domain of emotions. The history of emotion theories, current approaches, and the interaction between emotion and cognition.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 159: Psychology of Attitude Change and Social Influence

Review of classic and current research on attitudes, attitude change and persuasion. Increase appreciation for the ways that our thoughts, actions, and feelings are shaped and manipulated by social influences.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 161: Emotion (PSYCH 261)

(Graduate students register for 261.) The scientific study of emotion. Topics: models of emotion, emotion antecedents, emotional responses (facial, subjective, and physiological), functions of emotion, emotion regulation, individual differences, and health implications. Focus is on experimentally tractable ideas.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)

PSYCH 167: Seminar on Aggression

The causes and modification of individual and collective aggression. Major issues in aggression: social labeling of injurious conduct, social determinants of aggression, effects of the mass media, institutionally sanctioned violence, terrorism, psychological mechanisms of moral disengagement, modification of aggressive styles of behavior, and legal sanctions and deterrence doctrines.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 170: The Psychology of Communication About Politics in America (COMM 164, COMM 264, POLISCI 224L)

Focus is on how politicians and government learn what Americans want and how the public's preferences shape government action; how surveys measure beliefs, preferences, and experiences; how poll results are criticized and interpreted; how conflict between polls is viewed by the public; how accurate surveys are and when they are accurate; how to conduct survey research to produce accurate measurements; designing questionnaires that people can understand and use comfortably; how question wording can manipulate poll results; corruption in survey research.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Krosnick, J. (PI)

PSYCH 171: Research Seminar on Aging

Two quarter practicum exposes students to multiple phases of research by participating in a laboratory focusing on social behavior in adulthood and old age. Review of current research; participation in ongoing data collection, analysis, and interpretation. Prerequisites: 1, research experience, and consent of instructor. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 4 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Carstensen, L. (PI)

PSYCH 179: The Psychology of Everyday Morality (PSYCH 270)

(Graduate students register for 270.) For graduate students, coterms, and senior Psychology majors. Traditional approaches focusing on how morality colors mundane human activities such as eating and on morality as defined by actors themselves rather than social scientists. Moral hypocrisy, food and disgust, taboo trade-offs, moral reproach, and prejudice with compunction. Limited enrollment. Prerequisite: 70 and consent of instructor.
Terms: not given this year | Units: 4 | Grading: Letter or Credit/No Credit

PSYCH 183: Mind, Culture, and Society Research Core

Required of students in the mind, culture, and society specialization track. Research training on a variety of projects that explore how social identities such as race, class, gender, and culture affect psychological experiences across domains including education, law, business and health. Must participate for two consecutive quarters. Permission of instructor required. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 2-3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Ambady, N. (PI); Eberhardt, J. (PI); Markus, H. (PI)

PSYCH 186: The Psychology of Everyday Morality (PSYCH 286)

Recent literature on morality from a social psychological perspective. Topics include moral judgment, moral intuitions, moral hypocrisy, moral identity, moralization, moral reproach, shame and guilt, temptations, and self-regulation. Contemporary psychological research emphasizing descriptive approaches (what people actually do) rather than normative ones (what one should do).
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 189: Stanford Center on Longevity Practicum

Student involvement in an interdisciplinary center aimed at changing the culture of human aging using science and technology. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Carstensen, L. (PI)

PSYCH 190: Special Research Projects

May be repeated for credit. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr | Units: 1-6 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Monin, B. (PI)

PSYCH 192: Career and Personal Counseling (EDUC 134, EDUC 234)

Theories and methods for helping people create more satisfying lives for themselves. Simulated counseling experiences.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Krumboltz, J. (PI)

PSYCH 193: Special Laboratory Research

May be repeated for credit. Prerequisites: 1, 10, and consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 193A: Special Laboratory Research

May be repeated for credit. Prerequisites: 1, 10, and consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Ambady, N. (PI)

PSYCH 194: Reading and Special Work

Independent study. May be repeated for credit. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 195: Special Laboratory Projects

Independent study. May be repeated for credit. Prerequisites: 1, 10, and consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 196: Contemporary Psychology: Overview of Theory, Research, Applications

Capstone experience for juniors and seniors that bridges course work with research opportunities. Lectures representing the department's areas: social, personality, developmental, neuroscience, and cognitive psychology. Faculty present current research. Discussions led by advanced graduate students in the field represented by that week's guest. Students write research proposals. Small grants available to students to conduct a pilot study of their proposed research. Limited enrollment. Prerequisite: consent of instructor.
Terms: Aut | Units: 3 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Thomas, E. (PI); Paluy, Y. (TA); Russell, A. (TA)

PSYCH 197: Advanced Research

Limited to students in senior honors program. Weekly research seminar, independent research project under the supervision of an appropriate faculty member. A detailed proposal is submitted at the end of Autumn Quarter. Research continues during Winter and Spring quarters as 198. A report demonstrating sufficient progress is required at the end of Winter Quarter.
Terms: Aut | Units: 1-4 | Grading: Satisfactory/No Credit

PSYCH 198: Senior Honors Research

Limited to students in the senior honors program. Finishing the research and data analysis, written thesis, and presentation at the Senior Honors Convention. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-4 | Repeatable for credit | Grading: Letter (ABCD/NP)

PSYCH 199: Temptations and Self Control (PSYCH 299)

(Graduate students register for 299.) Why do people do things that that they come to regret? How can people minimize behavior such as exercise avoidance, angry words, overeating, unsafe sex, and dangerous driving? Sources include classical and current research from experimental psychology, neuroscience, behavioral economics, and neuroeconomics. Real-world applications.
Terms: not given this year | Units: 2 | Grading: Letter (ABCD/NP)

PSYCH 201: Social Psychology Lecture Series

Required of social psychology graduate students. Guest lecturers from Stanford and other institutions. May be repeated for credit. (Miller)
Terms: not given this year | Units: 3 | Grading: Satisfactory/No Credit

PSYCH 202: Cognitive Neuroscience

Graduate core course. The anatomy and physiology of the brain. Methods: electrical stimulation of the brain, neuroimaging, neuropsychology, psychophysics, single-cell neurophysiology, theory and computation. Neuronal pathways and mechanisms of attention, consciousness, emotion, language, memory, motor control, and vision. Prerequisite: 207 or consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Wandell, B. (PI)

PSYCH 203: MODELS OF LANGUAGE ACQUISITION

How do children learn to understand and produce their native language? Language acquisition is a core topic in cognitive science and has been a key test case for formal approaches. Topics include: learnability theory, grammatical approaches, connectionist models, and probabilistic models.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 204: Computation and cognition: the probabilistic approach

This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Goodman, N. (PI); Ouyang, L. (TA)

PSYCH 204A: Human Neuroimaging Methods

This course introduces the student to human neuroimaging using magnetic resonance scanners. The course is a mixture of lectures and hands-on software tutorials. The course begins by introducing basic MR principles. Then various MR measurement modalities are described, including several types of structural and functional imaging methods. Finally algorithms for analyzing and visualizing the various types of neuroimaging data are explained, including anatomical images, functional data, diffusion imaging (e.g., DTI) and magnetization transfer. Emphasis is on explaining software methods used for interpreting these types of data.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 204B: Computational Neuroimaging: Analysis Methods

Neuroimaging methods with focus on data analysis techniques. Basic MR physics and BOLD signals. Methods for neuroimaging data using real and simulated data sets. Topics include: linearity of the fmri signal; time versus space resolution tradeoffs; noise in neuroimaging; correlation analysis; visualization methods; cortical reconstruction, inflation, and flattening; reverse engineering; can cognitive states be predicted from brain activation? Prerequisite: consent of instructor.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Grill-Spector, K. (PI)

PSYCH 205: Foundations of Cognition

Topics: attention, memory, language, similarity and analogy, categories and concepts, learning, reasoning, and decision making. Emphasis is on processes that underlie the capacity to think and how these are implemented in the brain and modeled computationally. The nature of mental representations, language and thought, modular versus general purpose design, learning versus nativism. Prerequisite: 207 or consent of instructor.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: McClelland, J. (PI)

PSYCH 206: Cortical Plasticity: Perception and Memory

Seminar. Topics related to cortical plasticity in perceptual and memory systems including neural bases of implicity memory, recognition memory, visual priming, and perceptual learning. Emphasis is on recent research with an interdisciplinary scope, including theory, behavioral findings, neural mechanisms, and computational models. May be repeated for credit. Recommended: 30, 45
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 207: Professional Seminar for First-Year Ph.D. Graduate Students

Required of and limited to first-year Ph.D. students in Psychology. Major issues in contemporary psychology with historical backgrounds.
Terms: Aut | Units: 2-3 | Grading: Satisfactory/No Credit
Instructors: Gotlib, I. (PI)

PSYCH 207B: Professional Development Seminar in Psychology

For graduate students who wish to gain professional development skills to pursue an academic career. May be repeated for credit. Course is intended for second year Ph.D. student in Psychology but open to all years.
Terms: not given this year | Units: 0-1 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 208: Advanced Topics in Self-Defense

Seminar. Threat to the self and how people deal with them. Readings from social psychological areas including social comparison, self-affirmation, self-completion, self-discrepancy, shame and guilt, terror management, dimensions of self-worth, self-regulation, self-presentation, psychophysiology, and moral identity. Enrollment limited to 15.
Terms: not given this year | Units: 1-3 | Grading: Satisfactory/No Credit

PSYCH 209: Models of Cognitive Processes

For advanced undergraduates and graduate students. Models of cognitive and developmental processes, including perception, attention, memory, decision making, acting and thinking; and on modeling cognitive development, domain learning, and skill acquisition as processes that take place over time. Models considered will include parallel distributed processing models and other types of artificial neural network models as well as process models spanning a spectrum from abstract to neurally realistic. Students learn about classic models and carry out exercises in the first six weeks and will undertake projects and learn about recent developments during the last four weeks of the quarter. Recommended: computer programming ability, familiarity with differential equations, linear algebra, and probability theory, and courses in cognitive psychology and/or cognitive/systems neuroscience.
Terms: Win | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: McClelland, J. (PI); Rodriguez, C. (TA)

PSYCH 211: Developmental Psychology

Prerequisite: 207 or consent of instructor.
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 212: Social Psychology

Classic studies in experimental social psychology. Group and group dynamics; compliance and social pressure; conformity, cooperation, conflict, and social dilemmas; attraction and preference; attitudes and attitude change; social comparison, emotion, and affiliation; dissonance, consistency, and self-justification; attribution and self-perception; judgment and decision making, motivation, automaticity, and culture.
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 213: Affective Science

This seminar is the core graduate course on affective science. We consider definitional issues, such as differences between emotion and mood, as well as issues related to the function of affect, such as the role affect plays in daily life. We review autonomic, neural, genetic, and expressive aspects of affective responding. Later in the course we discuss the role of affect in cognitive processing, specifically how affective states direct attention and influence memory, as well as the role of affect in decision making. We will also discuss emotion regulation and the strategic control of emotion; the cultural shaping of emotional experience and regulation; disorders of emotion; and developmental trajectories of experience and control from early to very late life. Meetings are discussion based. Attendance and active participation are required. Prerequisite: 207 or consent of instructor.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 215: Mind, Culture, and Society

Social psychology from the context of society and culture. The interdependence of psychological and sociocultural processes: how sociocultural factors shape psychological processes, and how psychological systems shape sociocultural systems. Theoretical developments to understand social issues, problems, and polity. Works of Baldwin, Mead, Asch, Lewin, Burner, and contemporary theory and empirical work on the interdependence of psychology and social context as constituted by gender, ethnicity, race, religion, and region of the country and the world. Prerequisite: 207 or consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 216: Public Policy and Social Psychology: Implications and Applications (IPS 207B, PUBLPOL 305B)

Theories, insights, and concerns of social psychology relevant to how people perceive issues, events, and each other, and links between beliefs and individual and collective behavior. Topics include: situationist and subjectivist traditions of applied and theoretical social psychology; social comparison, dissonance, and attribution theories; social identity, stereotyping, racism, and sources of intergroup conflict and misunderstanding; challenges to universality assumptions regarding human motivation, emotion, and perception of self and others; the problem of producing individual and collective changes in norms and behavior.
Terms: Win | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Ross, L. (PI)

PSYCH 216A: Statistics and data analysis in MATLAB

This course will cover basic statistical principles that are widely useful for the analysis of neuroscience and behavioral data, such as error bars and confidence intervals, multivariate probability distributions, regression and classification, linear and nonlinear models, cross-validation, bootstrapping, and model selection. In each class, we will cover the theory behind a statistical principle and learn how to implement the principle efficiently in MATLAB. Example material can be found at http://randomanalyses.blogspot.com. Prerequisites: Familiarity with basic statistics and programming in MATLAB
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 217: Topics and Methods Related to Culture and Emotion

Preference to graduate students. How cultural factors shape emotion and other feeling states. Empirical and ethnographic literature, theories, and research on culture and emotion. Applications to clinical, educational, and occupational settings. Research in psychology, anthropology, and sociology. May be repeated for credit.
Terms: Win | Units: 1-3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Tsai, J. (PI)

PSYCH 218: Early Social Cognitive Development

Current literature on social and cognitive development in infancy emphasizing the interface between the two domains. May be repeated for credit. Prerequisite: consent of instructor.
Terms: not given this year | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 220: Special Topics in Cognitive Development

For graduate students and advanced undergraduates. How research from cognitive development, decision making, and preference change can inform interventions on important social issues. May be repeated for credit.
Terms: Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 220S: Temptations and Self Control (PSYCH 120S)

Why do people do things they come to regret, such as lack of exercise, angry words, overeating, unsafe sex, or dangerous driving? How can they minimize such behaviors? Sources include classical and current research from experimental psychology, neuroscience, behavioral economics, and neuroeconomics. Emphasis is on real-world applications.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 221: Applied Vision and Image Systems

This course is an introduction to imaging technologies including hardware and the image processing pipeline. There is an emphasis on how these technologies accommodate the requirements of the human visual system. The course is intended for students interested in various aspects of imaging technologies, including - Digital cameras and displays - Image processing and compression - Image quality analysis - Human color, pattern and motion vision The course consists of lectures, tutorials and a project. Lectures cover the tools used in digital imaging and image quality measurement. Tutorials and projects include extensive software simulations of the digital imaging pipeline. Some background in mathematics (linear algebra) and programming (Matlab) is valuable.
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Wandell, B. (PI)

PSYCH 223: Social Norms (OB 630)

This course covers research and theory on the origins and function of social norms. Topics include the estimation of public opinion, the function of norms as ideals and standards of judgment, and the impact of norms on collective and individual behavior. In addition to acquainting students with the various forms and functions of social norms the course will provide students with experience in identifying and formulating tractable research questions.
Terms: Win | Units: 4 | Grading: Letter (ABCD/NP)

PSYCH 224: Research Topics in Emotion Regulation

Current research findings and methods, ongoing student research, and presentations by visiting students and faculty. May be repeated for credit. Prerequisite: consent of instuctor.
Terms: not given this year | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 225: Special Neuroscience Seminar with Dr. Shinobu Kitayama

How will culture influence the human mind? Is culture a superficial overlay on the basic, universal computational machine called the mind? Alternatively, is culture a crucial constitutive element of the mind? If so, what are specific mechanisms underlying this constitution process? And what theoretical framework do we need to make a visible progress on these questions? More generally, how can we start discussing meaningfully and productively about various problematic dichotomies such as mind versus body, culture versus biology, and nurture versus nature? An emerging field of cultural neuroscience has the potential of addressing these and other important questions and thus bridging natural, behavioral, and social sciences of the human mind. This seminar reviews the field of cultural neuroscience. It starts with a discussion of some theoretical foundations of the field, including cultural psychology, cognitive and social neuroscience, evolutionary psychology, and population genetics (PART 1). We will then discuss several specific content domains with a focus on cross-cultural variations in brain responses (PART 2). The seminar will conclude with a discussion on gene x environment interaction in varying cultural contexts (PART 3). Students can take the seminar for credit. One unit for attending all five sessions, two units for all five session and a short paper.
Terms: not given this year | Units: 1-2 | Grading: Satisfactory/No Credit

PSYCH 226: Models and Mechanisms of Memory

Current topics in memory as explored through computational models addressing experimental findings and physiological and behavioral investigations. Topics include: explicit and inplict learning; role of MTL structures in learning and memory; and single versus dual processes approaches to recognition. May be repeated for credit.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 228: Ion Transport and Intracellular Messengers (PSYCH 121)

(Graduate students register for 228.) Ion channels, carriers, ion pumps, and their regulation by intracellular messengers in a variety of cell types. Recommended: 120, introductory course in biology or human biology.
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Wine, J. (PI)

PSYCH 231: Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives (COMM 339, POLISCI 421K)

The social and psychological processes involved in asking and answering questions via questionnaires for the social sciences; optimizing questionnaire design; open versus closed questions; rating versus ranking; rating scale length and point labeling; acquiescence response bias; don't-know response options; response choice order effects; question order effects; social desirability response bias; attitude and behavior recall; and introspective accounts of the causes of thoughts and actions.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

PSYCH 232: Brain and Decision Making

Neuroeconomics combines experimental techniques from neuroscience, psychology, and experimental economics, such as electrophysiology, fMRI, eye tracking, and behavioral studies, and models from computational neuroscience and economics. May be repeated for credit. Prerequisite: consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Knutson, B. (PI)

PSYCH 233: MATLAB and Psychtoolbox for the Behavioral Sciences

Topics such as experiment design, stimulus presentation, counterbalancing, response collection, data analysis, and plotting. Programming experiments. Final project programming a complete behavioral experiment relevant to student's research.
Terms: Spr | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Hennigan, K. (PI); Hoffmann Bion, R. (PI); Tang, G. (PI)

PSYCH 234: Topics in Depression

Current research topics including epidemiology and phenomenology of affective disorders, psychological theories of depression, gender differences in affective disorders, cognitive and social functioning of depressed persons, psychobiology of affective disorders, depression in children, postpartum depression, suicide issues in the treatment of depression, and cultural aspects of affective disorders. Prerequisite: graduate standing in Psychology or consent of instructor. May be repeated for credit
Terms: not given this year | Units: 1-3 | Repeatable for credit | Grading: Letter (ABCD/NP)

PSYCH 236C: SEM IN SEMANTICS: Representations of meaning (LINGUIST 236)

Representations of meaning from psychological, linguistic, and computational viewpoints. Topics include lambda calculus, probabilistic programming, and vector spaces. Special emphasis on the challenges of semantic composition. May be repeated for credit.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 238: Wise Interventions (PSYCH 138)

Classic and contemporary psychological interventions; the role of psychological factors in social reforms for social problems involving healthcare, the workplace, education, intergroup, relations, and the law. Topics include theories of intervention, the role of laboratory research, evaluation, and social policy.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Walton, G. (PI)

PSYCH 239: Formal and Computational Approaches in Psychology and Cognitive Science

Do psychology and cognitive science need formal theories and/or explicit computational models? What insights should such things provide? What is the proper relationship between different theoretical and modeling approaches? Between different levels or kinds of analysis? Where do informally stated theories fit in and what are the roles of formal and computational modeling approaches in relation to other less explicitly specified forms of theorizing? This seminar will explore these issues and compare different formal and computational model variants, especially connectionist and probabilistic models, within 3-4 different target domains. Possible target domains include categorization, property induction, causal learning, perceptual decision making, language acquisition, semantics and pragmatics, and mid-level vision.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 243: General Development Seminar

May be repeated for credit. Prerequisite: consent of instructors. Restricted to Developmental graduate students.
Terms: Win | Units: 1-2 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Markman, E. (PI)

PSYCH 244: Psychology of Aging

Theory and research in gerontology. Normal and abnormal changes that occur in biological, cognitive, and psychological aging. Emphasis is on the environmental factors that influence the aging process. Prerequisite: graduate standing in Psychology or consent of instructor.
Terms: not given this year | Units: 1-3 | Grading: Letter (ABCD/NP)

PSYCH 245: Social Psychological Perspectives on Stereotyping and Prejudice

Classic and contemporary social psychological approaches to prejudice and stereotyping. Emphasis is on how stereotypes are employed and maintained, and the influence of stereotyping and prejudice on behavior in domains including education, employment, politics, and law. Limited enrollment.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 246: Cognitive and Neuroscience Friday Seminar

Participant presentations. May be repeated for credit. Prerequisite: graduate standing in psychology or neuroscience program.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Wagner, A. (PI)

PSYCH 247: Fundamentals of Neuroscience for Non-Life-Scientists

Human behavior and the human brain and how it enables perception, learning, decision making, planning, and action with a focus on how neuroscience may be presented or used in law, business, or education contexts. Neurotechnology and experimental methods used to conduct research.
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

PSYCH 249: Human Motivation

Current research and theory including questions concerning the nature of human motives, intrinsic motivation, self-regulation, the roles of affect and cognition, and lifespan and cultural influences on motivation. Prerequisite: 207 or consent of instructors.
Terms: not given this year | Units: 1-3 | Grading: Letter (ABCD/NP)

PSYCH 250: High-Level Vision: Object Representation (CS 431)

(Formerly CS423 High-Level Vision: Behaviors, Neurons, and Computational Models) Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, Computer Science and Applied Statistics. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field. Topics include: theories of object perception, neural computations underlying invariant object perception, how visual exemplars and categories are represented in the brain, what information is present in distributed activations across neural populations and how it relates to object perception, what modern statistical and analytical tools there are for multi-variate analysis of brain activations.
Terms: not given this year | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 251: Affective Neuroscience

Theory and research. Comparative and human research approaches map affective function to neuroanatomical and neurochemical substrates. Prerequisite: consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Knutson, B. (PI)

PSYCH 252: Statistical Methods for Behavioral and Social Sciences

For students who seek experience and advanced training in empirical research. Analysis of data from experimental through factorial designs, randomized blocks, repeated measures; regression methods through multiple regression, model building, analysis of covariance; categorical data analysis through two-way tables. Integrated with the use of statistical computing packages. Prerequisite: 10 or equivalent.
Terms: Aut | Units: 1-6 | Grading: Letter or Credit/No Credit
Instructors: Monin, B. (PI); Thomas, E. (PI); Fu, A. (TA)

PSYCH 253: Statistical Theory, Models, and Methodology

Practical and theoretical advanced data analytic techniques such as loglinear models, signal detection, meta-analysis, logistic regression, reliability theory, and factor analysis. Prerequisite: 252 or EDUC 257.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Thomas, E. (PI)

PSYCH 254: Lab in Experimental Methods

Laboratory class in experimental methods for psychology, with a focus on technical/computer-based methods. Programming experience helpful although not required. Topics include data collection on the web, data management and data analysis.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Frank, M. (PI)

PSYCH 257: Individually Supervised Practicum

Satisfies INS requirements for curricular practical training. Relevant experience for graduate students as part of their program of study. May be repeated for credit. Prerequisites: graduate standing in Psychology, consent of adviser. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 3-5 | Repeatable for credit | Grading: Letter (ABCD/NP)

PSYCH 258: Graduate Seminar in Social Psychology Research

For students who are already or are planning to become involved in research on social construal and the role that it plays in a variety of phenomena, notably the origin and escalation of conflict.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Markus, H. (PI)

PSYCH 259: Emotions: History, Theories, and Research (PSYCH 158)

Graduate students register for 259. Theoretical and empirical issues in the domain of emotions. The history of emotion theories, current approaches, and the interaction between emotion and cognition.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 261: Emotion (PSYCH 161)

(Graduate students register for 261.) The scientific study of emotion. Topics: models of emotion, emotion antecedents, emotional responses (facial, subjective, and physiological), functions of emotion, emotion regulation, individual differences, and health implications. Focus is on experimentally tractable ideas.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 261A: Learning and Cognition in Activity (EDUC 295)

Methods and results of research on learning, understanding, reasoning, problem solving, and remembering, as aspects of participation in social organized activity. Principles of coordination that support cognitive achievements and learning in activity settings in work and school environments.
Terms: not given this year | Units: 3 | Grading: Satisfactory/No Credit

PSYCH 262: Language and Thought (PSYCH 131)

The psychology of language including: production and understanding in utterances; from speech sounds to speaker's meaning; children's acquisition of the first language; and the psychological basis for language systems. Language functions in natural contexts and their relation to the processes by which language is produced, understood, and acquired. Prerequisite: 1 or LINGUIST 1.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Henetz, T. (PI)

PSYCH 265: Social Psychology and Social Change (EDUC 371X)

The course is intended an exploration of the major ideas, theories, and findings of social psychology and their applied status. Special attention will be given to historical issues, classic experiments, and seminal theories, and their implications for topics relevant to education. Contemporary research will also be discussed. Advanced undergraduates and graduate students from other disciplines are welcome.
Terms: Spr | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Cohen, G. (PI)

PSYCH 267: Human Memory: Facts, Fallacies, and Fragile Powers

Seminar. Applications of memory concepts in everyday life and in social and clinical settings. Topics include personal identity, childhood amnesia, autobiographic memory, emotions and memory, memory distortions, illusions, self-serving biases, recovery of repressed memories, false memories, implicit memories, and unconscious influences on social behavior, with applications to psychopathology.
Terms: not given this year | Units: 1-3 | Grading: Satisfactory/No Credit

PSYCH 269: Graduate Seminar in Affective Science

May be repeated for credit. Prerequisite: graduate standing in Psychology. (Tsai)
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Tsai, J. (PI)

PSYCH 270: The Psychology of Everyday Morality (PSYCH 179)

(Graduate students register for 270.) For graduate students, coterms, and senior Psychology majors. Traditional approaches focusing on how morality colors mundane human activities such as eating and on morality as defined by actors themselves rather than social scientists. Moral hypocrisy, food and disgust, taboo trade-offs, moral reproach, and prejudice with compunction. Limited enrollment. Prerequisite: 70 and consent of instructor.
Terms: not given this year | Units: 4 | Grading: Letter or Credit/No Credit

PSYCH 272: Special Topics in Psycholinguistics

May be repeated for credit. Prerequisite: consent of instructor.
Terms: not given this year | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 273: Graduate Seminar on Language, Cognition, and Perception

Current topics and debates. Readings from psychology, linguistics, neuroscience, ethology, anthropology, and philosophy. May be repeated for credit.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 274: Graduate Research Workshop on Psychological Interventions (EDUC 287X)

Psychological research has the potential to create novel interventions that promote the public good. This workshop will expose students to psychologically 'wise' intervention research and to support their efforts to conduct such interventions, especially in the context of education, broadly conceived, as well as other areas. The first part of the class will address classic interventions and important topics in intervention research, including effective delivery mechanisms, sensitive behavioral outcomes, the role of theory and psychological process, and considerations of the role of time and of mechanisms that can sustain treatment effects over time. In the second part of the class, students will present and receive feedback on their own ongoing and/or future intervention research. Prerequisite: Graduate standing in Psychology or Education, or consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 275: Graduate Research

Intermediate-level research undertaken with members of departmental faculty. Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 279: Topics in Cognitive Control

The processes that enable flexible behavior by biasing contextually relevant perceptual, mnemonic, and response representations or processing pathways. Cognitive control is central to volitional action, allowing work with memory, task/goal states, and overriding inappropriate responses. Current models of cognitive control, functional neuroimaging, and neuropsychological evidence. Recommended: 45. May be repeated for credit.
Terms: Aut | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Wagner, A. (PI)

PSYCH 281: Practicum in Teaching

Enrollment limited to teaching assistants in selected Psychology courses. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 282: Practicum in Teaching PSYCH 1

Logistical TA training including: preparing for sections; creating, correcting exams; grading an iterative writing assignment; office hours; review sessions; developing audiovisual expertise; communicating via coursework. Review of student evaluations with instructor to set goals and strategies. Second quarter focuses on pedagogical improvement. Limited to current PSYCH 1 TAs. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-2 | Repeatable for credit | Grading: Satisfactory/No Credit

PSYCH 284: Computational Modeling of a Range of Neural Circuits

Lectures, student presentations, and extensive software exercises. Focus on quantifiable models of neural signaling, starting with physical specification of input signals, sensory transductions, spiking, and mean electrical field potentials, and the inter-relation to BOLD signals (fMRI). Applications will be drawn from many examples, but a there will be a particular focus on the visual pathways and how measurements and models relate to visual perception.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 286: The Psychology of Everyday Morality (PSYCH 186)

Recent literature on morality from a social psychological perspective. Topics include moral judgment, moral intuitions, moral hypocrisy, moral identity, moralization, moral reproach, shame and guilt, temptations, and self-regulation. Contemporary psychological research emphasizing descriptive approaches (what people actually do) rather than normative ones (what one should do).
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 288: Hierarchical Linear Modeling for Psychological Sciences

HLM is a statistical theory and a computer program used to analyze multi-level data, such as trials within participants or students within classrooms. HLM allows researchers to analyze data at each level of analysis separately, to partition the total variance across different levels, to explain variance at each level separately using level-appropriate predictors, and to model cross-level interactions. How to use the HLM program and to model various types of multi-level data using it. May be repeated for credit.
Terms: not given this year | Units: 1-3 | Grading: Satisfactory/No Credit

PSYCH 289: Sensory Representations in Language and Memory

Is recollecting an experience similar to re-experiencing it? How closely tied is our knowledge to the perceptual representations and processes that may have given rise to it? What role do perceptuo-motor representations play in understanding language? We will review the recent literature on perceptual re-activation in episodic memory, perceptual grounding in semantic representations, and neural reuse of perceptual mechanisms for abstract thought. Emphasis will be placed on recent research with an interdisciplinary scope, including discussion of theory, behavioral findings, neural mechanisms, and computational models. Prerequisite: Psych 207 or consent of instructor.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 290: Graduate Research Methods

Primary tool use for psychologists: basics of experiment design; computer-based experiments; web-based experiments; data analysis packages and data presentation; exploratory statistics; eye-tracking methods; psychophysiology methods; survey construction; corpus and discourse analysis; and perhaps hypnosis. Prerequisite: Ph.D. student in Psychology.
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

PSYCH 291: Psychology Teaching Methods

Open to graduate students and advanced undergraduates. Principles of good teaching. Students practice teaching skills.
Terms: not given this year | Units: 1-2 | Grading: Satisfactory/No Credit

PSYCH 293: Communication, Intentionality, and the Origins of Language

How did language evolve to become a ubiquitous, definitional part of human life? What relationship does children's early language have to their understanding of intentionality and other methods of non-verbal communication? This seminar will survey theoretical and experimental work on the foundations of human language, communication, and intentionality, with the goal of understanding what we know and what questions are still open. Areas of focus include developmental work on communication; whether early language use is referential/intentional and whether early words are general or particular; and research on language evolution and animal communication.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Frank, M. (PI)

PSYCH 294: Human Prosociality

Human beings engage in a vast amount of prosocial behaviors (including altruism and cooperation) that critically support our success as a social species. That said, the psychological underpinnings of prosociality remain surprisingly enigmatic. This seminar will survey classic and modern theories of prosocial behavior from evolutionary biology, economics, psychology, and neuroscience, with an emphasis on common ideas about the cognitive and affective mechanisms supporting such behaviors. Students will be responsible for leading discussions and producing one in-depth review or research paper at the end of the quarter.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Zaki, J. (PI)

PSYCH 297: Seminar for Coterminal Master of Arts

Contemporary issues and student research. Student and faculty presentations.
Terms: Spr | Units: 1-2 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Thomas, E. (PI)

PSYCH 299: Temptations and Self Control (PSYCH 199)

(Graduate students register for 299.) Why do people do things that that they come to regret? How can people minimize behavior such as exercise avoidance, angry words, overeating, unsafe sex, and dangerous driving? Sources include classical and current research from experimental psychology, neuroscience, behavioral economics, and neuroeconomics. Real-world applications.
Terms: not given this year | Units: 2 | Grading: Letter (ABCD/NP)

PSYCH 303: Human and Machine Hearing

Topics: Linear and nonlinear system theory applied to sound and hearing; understanding how to model human hearing in the form of algorithms that can process general sounds efficiently; how to construct, display, and interpret "auditory images"; how to extract features compatible with machine-learning systems; how to build systems that extract information from sound to do a job; and example applications of machine hearing to speech, music, security and surveillance, personal sound diaries, smart house, etc. Prerequisites: basic calculus and algorithms.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 373: Research Seminar: Mind, Brain, and Computation

Faculty and student research presentations focusing on work linking cellular, systems, cognitive, behavioral, and computational neuroscience. Limited to affiliates of the Center for Mind, Brain and Computation. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: McClelland, J. (PI)

PSYCH 380: Collaborating with the Future: Launching Large Scale Sustainable Transformations (ENVRES 380, ME 380)

This project-based d.school class combines Design Thinking Processes, Behavioral Sciences, and elements of Diffusion Theory. Tools and theories introduced in class will be used to structure large-scale transformations that simultaneously create value on environmental, societal, and economic fronts. We encourage students to use this class as a launching pad for real initiatives. Primarily meant for Graduate Students. (Especially qualified/motivated Seniors will be considered). Admission to the class is through an application process which ends on March 3. Please find instructions and applications at https://dschool.stanford.edu/groups/largetransformations/.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Ambady, N. (PI); Banerjee, S. (PI); Staff, 1. (PI)

PSYCH 383: International Conflict: Management and Resolution (IPS 250)

(Same as LAW 656) Interdisciplinary. Theoretical insights and practical experience in resolving inter-group and international conflicts. Sources include social psychology, political science, game theory, and international law. Personal, strategic, and structural barriers to solutions. How to develop a vision of a mutually bearable shared future, trust in the enemy, and acceptance of loss that a negotiated settlement may produce. Spoilers who seek to sabotage agreements. Advantages and disadvantages of unilateral versus reciprocal measures. Themes from the Stanford Center of International Conflict and Negotiation (SCICN). Prerequisite for undergraduates: consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

PSYCH 459: Frontiers in Interdisciplinary Biosciences (BIO 459, BIOC 459, BIOE 459, CHEM 459, CHEMENG 459)

Students register through their affiliated department; otherwise register for CHEMENG 459. For specialists and non-specialists. Sponsored by the Stanford BioX Program. Three seminars per quarter address scientific and technical themes related to interdisciplinary approaches in bioengineering, medicine, and the chemical, physical, and biological sciences. Leading investigators from Stanford and the world present breakthroughs and endeavors that cut across core disciplines. Pre-seminars introduce basic concepts and background for non-experts. Registered students attend all pre-seminars; others welcome. See http://biox.stanford.edu/courses/459.html. Recommended: basic mathematics, biology, chemistry, and physics.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Robertson, C. (PI)

PSYCH 801: Master's TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

PSYCH 802: PhD TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

STATS 42Q: Undergraduate Admissions to Selective Universities - a Statistical Perspective

The goal is the building of a statistical model, based on applicant data, for predicting admission to selective universities. The model will consider factors such as gender, ethnicity, legacy status, public-private schooling, test scores, effects of early action, and athletics. Common misconceptions and statistical pitfalls are investigated. The applicant data are not those associated with any specific university.
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

STATS 48N: Riding the Data Wave

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Sabatti, C. (PI)

STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Bacallado de Lara, S. (PI); Baiocchi, M. (PI); Richards, W. (PI); Thomas, E. (PI); Walther, G. (PI)

STATS 90: Mathematics and Statistics in the Real World (MATH 16)

This is an introductory quantitative literacy course, that offers an introduction to the mathematics (outside of calculus) used in real-world problems. Topics include: (a) Exponential functions, compound interest, population growth. (b) Geometric series, applications to mortgage payments, amortization of loans, present value of money, drug doses and blood levels. (c) First-order approximation, estimating areas and volumes. (d) Basic probability: Bayes's rule, false positives in disease detection and drug testing. (e) Basic descriptive statistics: mean, median, standard deviation f) Least squares and linear regression.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khare, A. (PI); Rajaratnam, B. (PI)

STATS 110: Statistical Methods in Engineering and the Physical Sciences

Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum | Units: 4-5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Head, A. (PI); Reeves, G. (PI)

STATS 116: Theory of Probability

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent.
Terms: Aut, Spr, Sum | Units: 3-5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Bacallado de Lara, S. (PI); Ben-David, E. (PI); Romano, J. (PI)

STATS 141: Biostatistics (BIO 141)

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut | Units: 3-5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Li, L. (PI)

STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | Grading: Letter or Credit/No Credit
Instructors: Bacallado de Lara, S. (PI); Baiocchi, M. (PI); Richards, W. (PI); Thomas, E. (PI); Walther, G. (PI)

STATS 166: Computational Algorithms for Statistical Genetics (GENE 245, STATS 345)

Computational algorithms for human genetics research. Topics include: permutation, bootstrap, expectation maximization, hidden Markov model, and Markov chain Monte Carlo. Rationales and techniques illustrated with existing implementations commonly used in population genetics research, disease association studies, and genomics analysis. Prerequisite: GENE 244 or consent of instructor.
Terms: Spr, not given next year | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

STATS 167: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 267)

Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Terms: Spr | Units: 4 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit

STATS 191: Introduction to Applied Statistics

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Taylor, J. (PI)

STATS 198: Practical Training

For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

STATS 199: Independent Study

For undergraduates.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 200: Introduction to Statistical Inference

Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: D'Aristotile, A. (PI); Fithian, W. (PI)

STATS 202: Data Mining and Analysis

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Patel, R. (PI); Taylor, J. (PI)

STATS 203: Introduction to Regression Models and Analysis of Variance

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Johnstone, I. (PI)

STATS 205: Introduction to Nonparametric Statistics

Nonparametric analogs of the one- and two-sample t-tests and analysis of variance; the sign test, median test, Wilcoxon's tests, and the Kruskal-Wallis and Friedman tests, tests of independence. Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 206: Applied Multivariate Analysis

Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre- or corequisite: 200.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khalessi, S. (PI)

STATS 207: Introduction to Time Series Analysis

Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Donoho, D. (PI)

STATS 208: Introduction to the Bootstrap

The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and confidence intervals; related resampling methods including the jackknife, cross-validation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

STATS 209: Understanding Statistical Models and their Social Science Applications (EDUC 260X, HRP 239)

Critical examination of statistical methods in social science applications, especially for cause and effect determinations. Topics: path analysis, multilevel models, matching and propensity score methods, analysis of covariance, instrumental variables, compliance, longitudinal data, mediating and moderating variables. See http://www-stat.stanford.edu/~rag/stat209. Prerequisite: intermediate-level statistical methods
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Rogosa, D. (PI)

STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, MED 206)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win | Units: 3 | Grading: Medical Satisfactory/No Credit

STATS 212: Applied Statistics with SAS

Data analysis and implementation of statistical tools in SAS. Topics: reading in and describing data, categorical data, dates and longitudinal data, correlation and regression, nonparametric comparisons, ANOVA, multiple regression, multivariate data analysis, using arrays and macros in SAS. Prerequisite: statistical techniques at the level of STATS 191 or 203; knowledge of SAS not required.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 213: Introduction to Graphical Models

Multivariate Normal Distribution and Inference, Wishart distributions, graph theory, probabilistic Markov models, pairwise and global Markov property, decomposable graph, Markov equivalence, MLE for DAG models and undirected graphical models, Bayesian inference for DAG models and undirected graphical models. Prerequisites: STATS 116, MATH 104 or equivalent class in linear algebra.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 215: Statistical Models in Biology

Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as in STATS 217, but emphasis is on examples drawn from applications in biology, especially genetics. Prerequisite: 116 or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Siegmund, D. (PI)

STATS 217: Introduction to Stochastic Processes

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of instructor.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khare, A. (PI); Rajaratnam, B. (PI)

STATS 218: Introduction to Stochastic Processes

Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bogdan, K. (PI)

STATS 219: Stochastic Processes (MATH 136)

Introduction to measure theory, Lp spaces and Hilbert spaces. Random variables, expectation, conditional expectation, conditional distribution. Uniform integrability, almost sure and Lp convergence. Stochastic processes: definition, stationarity, sample path continuity. Examples: random walk, Markov chains, Gaussian processes, Poisson processes, Martingales. Construction and basic properties of Brownian motion. Prerequisite: STATS 116 or MATH 151 or equivalent. Recommended: MATH 115 or equivalent.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Camilier, I. (PI)

STATS 221: Introduction to Mathematical Finance

Interest rate and discounted value. Financial derivatives, hedging, and risk management. Stochastic models of financial markets, introduction to Ito calculus and stochastic differential equations. Black-Scholes pricing of European options. Optimal stopping and American options. Prerequisites: MATH 53, STATS 116, or equivalents.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

STATS 222: Statistical Methods for Longitudinal Data (EDUC 351A)

Research designs and statistical procedures for time-ordered (repeated-measures) data. The analysis of longitudinal panel data is central to empirical research on learning and development. Topics: measurement of change, growth curve models, analysis of durations including survival analysis, experimental and non-experimental group comparisons, reciprocal effects, stability. See http://www-stat.stanford.edu/~rag/stat222/ . Prerequisite: intermediate statistical methods.
Terms: Spr | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Rogosa, D. (PI)

STATS 231: Statistical Learning Theory (CS 229T)

(Same as STATS 231) For a given learning problem, what methods should be employed, and under what assumptions can we expect them to work? This course focuses on developing algorithms for various scenarios (e.g., high-dimensional, online, unsupervised) as well as theoretical analyses of these algorithms. Topics include kernel methods, generalization bounds, spectral methods, online learning, and nonparametric Bayes. Prerequisites: A solid background in linear algebra and probability theory. Basic exposure to statistics and machine learning (STAT 315A or CS 229), and graphical models (CS 228) is helpful but not essential.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Liang, P. (PI)

STATS 237: Theory of Investment Portfolios and Derivative Securities

Asset returns and their volatilities. Markowitz¿s portfolio theory, capital asset pricing model, multifactor pricing models. Measures of market risk. Financial derivatives and hedging. Black¿Scholes pricing of European options. Valuation of American options. Implied volatility and the Greeks. Prerequisite: STATS 116 or equivalent
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Camilier, I. (PI)

STATS 238: Policy & Strategy Issues in Financial Engineering

(Same as LAW 564). This is a non-technical course that will focus on a series of case studies each designed to illuminate a serious public policy issue raised by the evolution of modern financial engineering. These will include discussions of Freddie Mac, Fannie Mae, sub-prime and Alt-A mortgages and the flaws of AAA CDOs; the spectacular losses by Orange County and the Florida Local Government Investment Pool and the challenges posed by unregulated investment pools; how credit default swaps are likely to change with central clearing using the PIIGS (Portugal/ Ireland/ Iceland/ Greece/ Spain), the monolines, AIG, Lehman and MF Global as examples; views of rogue trading using the similarities and disparities of Askin, Madoff, Barings, Soc Gen and UBS for discussion; and Risk Management 101 : the why/ how/ where/ when firms went wrong plus what to keep and what to throw out in the next phase of risk programs among other case studies. The subject matter, by necessity, is multi-disciplinary and so the course is particularly suited to those students having an interest in public policy and the evolution of modern financial markets. This includes students from the law or business schools, or the public policy, economics, EES, political science, or financial math and engineering programs among others. Several themes will tie the case studies, reading and discussions together:-Is this an example of an innovation that got too far ahead of existing operations, risk management, legal, accounting, regulatory or supervisory oversight?-How might temporary infrastructure be implemented without stifling innovation or growth?-How might losses be avoided by requiring permanent infrastructure sooner? Will Dodd-Frank, Basel III, etc., help to prevent such problems? What are the potential unintended consequences?-Is this an example of improperly viewing exposures that are subject to uncertainty or incorrectly modeling risk or both? Guest speakers will be invited to share their experiences. This course will aim to provide a practitioner(s) view of financial engineering over the past 3 ½ decades as well as a broad understanding of what went right and what went wrong plus cutting edge views of the future of financial engineering. Prerequisite: STATS 237 or equivalent and consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Beder, T. (PI)

STATS 239A: Workshop in Quantitative Finance

Topics of current interest.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Camilier, I. (PI)

STATS 240: Statistical Methods in Finance

(SCPD students register for 240P.) Regression analysis and applications to investment models. Principal components and multivariate analysis. Likelihood inference and Bayesian methods. Financial time series. Estimation and modeling of volatilities. Statistical methods for portfolio management. Prerequisite: STATS 200 or equivalent.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Lai, T. (PI)

STATS 240P: Statistical Methods in Finance

For SCPD students; see 240.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lai, T. (PI)

STATS 241: Financial Modeling Methodology and Applications

(SCPD students register for 241P.) Substantive and empirical modeling approaches in options and interest rate markets. Nonlinear least squares and nonparametric regression. Multivariate time series modeling and forecasting. Applications of canonical correlation analysis and cointegration. Statistical trading strategies and their evaluation. Prerequisite: 240 or equivalent.
Terms: given next year | Units: 3-4 | Grading: Letter or Credit/No Credit

STATS 241P: Financial Modeling Methodology and Applications

For SCPD students; see 241.
Terms: given next year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 242: Algorithmic Trading and Quantitative Strategies

An introduction to financial trading strategies based on methods of statistical arbitrage that can be automated. Methodologies related to high frequency data and stylized facts on asset returns; models of order book dynamics and order placement, dynamic trade planning with feedback; momentum strategies, pairs trading. Emphasis on developing and implementing models that reflect the market and behavioral patterns. Prerequisite: STATS 240 or equivalent.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Velu, R. (PI)

STATS 243: Statistical Models and Methods for Risk Management and Surveillance

(SCPD students register for 243P.) Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite: STATS 240 or equivalent.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Lai, T. (PI)

STATS 243P: Statistical Models and Methods for Risk Management and Surveillance

For SCPD students; see 243.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lai, T. (PI)

STATS 250: Mathematical Finance (MATH 238)

Stochastic models of financial markets. Forward and futures contracts. European options and equivalent martingale measures. Hedging strategies and management of risk. Term structure models and interest rate derivatives. Optimal stopping and American options. Corequisites: MATH 236 and 227 or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Papanicolaou, G. (PI)

STATS 253: Spatial Statistics (STATS 352)

Statistical descriptions of spatial variability, spatial random functions, grid models, spatial partitions, spatial sampling, linear and nonlinear interpolation and smoothing with error estimation, Bayes methods and pattern simulation from posterior distributions, multivariate spatial statistics, spatial classification, nonstationary spatial statistics, space-time statistics and estimation of time trends from monitoring data, spatial point patterns, models of attraction and repulsion. Applications to earth and environmental sciences, meteorology, astronomy, remote-sensing, ecology, materials.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 260A: Workshop in Biostatistics (HRP 260A)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Aut | Units: 1-2 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

STATS 260B: Workshop in Biostatistics (HRP 260B)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Win | Units: 1-2 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

STATS 260C: Workshop in Biostatistics (HRP 260C)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Spr | Units: 1-2 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

STATS 261: Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261)

Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Sainani, K. (PI)

STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Sainani, K. (PI)

STATS 267: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 167)

Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Terms: Spr | Units: 4 | Grading: Letter or Credit/No Credit

STATS 270: A Course in Bayesian Statistics (STATS 370)

Advanced-level Bayesian statistics. Topics: Discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. Examination of the construction of priors and the asymptotic properties of likelihoods and posterior densities. Discussion including but not limited to the case of finite dimensional parameter space. Prerequisite: familiarity with standard probability and multivariate distribution theory.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Sabatti, C. (PI)

STATS 290: Paradigms for Computing with Data

Advanced programming and computing techniques to support projects in data analysis and related research. For Statistics graduate students and others whose research involves data analysis and development of associated computational software. Prerequisites: Programming experience including familiarity with R; computing at least at the level of CS 106; statistics at the level of STATS 110 or 141.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Narasimhan, B. (PI)

STATS 297: Practical Training

For students in the M.S. program in Financial Mathematics only. Students obtain employment, with the approval and supervision of a faculty member, in a relevant industrial or research activity to enhance their professional experience. Students must submit a written final report upon completion of the internship in order to receive credit. May be repeated for credit once. Prerequisite: consent of adviser.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Lai, T. (PI)

STATS 298: Industrial Research for Statisticians

Masters-level research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an off-campus employer. Students must submit a written final report upon completion of the internship in order to receive credit. Prerequisite: enrollment in Statistics M.S. or Ph.D. program, prior to candidacy.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

STATS 299: Independent Study

For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit | Grading: Letter or Credit/No Credit

STATS 300: Advanced Topics in Statistics

May be repeated for credit.
Terms: Sum | Units: 2-3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Griffiths, R. (PI)

STATS 300A: Theory of Statistics

Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: Neyman-Pearson theory; UMP tests and uniformly most accurate confidence intervals; use of unbiasedness and invariance to eliminate nuisance parameters. Large sample theory: basic convergence concepts; robustness; efficiency; contiguity, locally asymptotically normal experiments; convolution theorem; asymptotically UMP and maximin tests. Asymptotic theory of likelihood ratio and score tests. Rank permutation and randomization tests; jackknife, bootstrap, subsampling and other resampling methods. Further topics: sequential analysis, optimal experimental design, empirical processes with applications to statistics, Edgeworth expansions, density estimation, time series.
Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Romano, J. (PI)

STATS 300B: Theory of Statistics

Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: Neyman-Pearson theory; UMP tests and uniformly most accurate confidence intervals; use of unbiasedness and invariance to eliminate nuisance parameters. Large sample theory: basic convergence concepts; robustness; efficiency; contiguity, locally asymptotically normal experiments; convolution theorem; asymptotically UMP and maximin tests. Asymptotic theory of likelihood ratio and score tests. Rank permutation and randomization tests; jackknife, bootstrap, subsampling and other resampling methods. Further topics: sequential analysis, optimal experimental design, empirical processes with applications to statistics, Edgeworth expansions, density estimation, time series.
Terms: Win | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Siegmund, D. (PI)

STATS 300C: Theory of Statistics

Decision theory formulation of statistical problems. Minimax, admissible procedures. Complete class theorems ("all" minimax or admissible procedures are "Bayes"), Bayes procedures, conjugate priors, hierarchical models. Bayesian non parametrics: diaichlet, tail free, polya trees, bayesian sieves. Inconsistency of bayes rules.
Terms: Spr | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Candes, E. (PI)

STATS 302: Qualifying Exams Workshop

Prepares Statistics Ph.D. students for the qualifying exams by reviewing relevant course topics and problem solving strategies.
Terms: Sum | Units: 3 | Grading: Credit/No Credit

STATS 303: PhD First Year Student Workshop

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 305: Introduction to Statistical Modeling

Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and Gauss-Markov theorem. Computation via QR decomposition, Gramm-Schmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage & influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114.
Terms: Aut | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Hastie, T. (PI)

STATS 306A: Methods for Applied Statistics

Regression modeling extended to categorical data. Logistic regression. Loglinear models. Generalized linear models. Discriminant analysis. Categorical data models from information retrieval and Internet modeling. Prerequisite: 305 or equivalent.
Terms: Win | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Efron, B. (PI)

STATS 306B: Methods for Applied Statistics: Unsupervised Learning

Unsupervised learning techniques in statistics, machine learning, and data mining.
Terms: Spr | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Taylor, J. (PI)

STATS 310A: Theory of Probability (MATH 230A)

Mathematical tools: sigma algebras, measure theory, connections between coin tossing and Lebesgue measure, basic convergence theorems. Probability: independence, Borel-Cantelli lemmas, almost sure and Lp convergence, weak and strong laws of large numbers. Large deviations. Weak convergence; central limit theorems; Poisson convergence; Stein's method. Prerequisites: 116, MATH 171.
Terms: Aut | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Montanari, A. (PI)

STATS 310B: Theory of Probability (MATH 230B)

Conditional expectations, discrete time martingales, stopping times, uniform integrability, applications to 0-1 laws, Radon-Nikodym Theorem, ruin problems, etc. Other topics as time allows selected from (i) local limit theorems, (ii) renewal theory, (iii) discrete time Markov chains, (iv) random walk theory, (v) ergodic theory. Prerequisite: 310A or MATH 230A.
Terms: Win | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Dembo, A. (PI)

STATS 310C: Theory of Probability (MATH 230C)

Continuous time stochastic processes: martingales, Brownian motion, stationary independent increments, Markov jump processes and Gaussian processes. Invariance principle, random walks, LIL and functional CLT. Markov and strong Markov property. Infinitely divisible laws. Some ergodic theory. Prerequisite: 310B or MATH 230B.
Terms: Spr | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Diaconis, P. (PI)

STATS 314: Advanced Statistical Methods

Topic this year is multiple hypothesis testing. The demand for new methodology for the simultaneous testing of many hypotheses as driven by modern applications in genomics, imaging, astronomy, and finance. High dimensionality: how tests of many hypotheses may be considered simultaneously. Classical techniques, and recent developments. Stepwise methods, generalized error rates such as the false discovery rate, and the role of resampling. May be repeated for credit.
Terms: not given this year | Units: 2-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

STATS 315A: Modern Applied Statistics: Learning

Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization. Kernel methods. Generalized additive models. Kernel smoothing. Gaussian mixtures and the EM algorithm. Model assessment and selection: crossvalidation and the bootstrap. Pathwise coordinate descent. Sparse graphical models. Prerequisites: STATS 305, 306A,B or consent of instructor.
Terms: Win | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Tibshirani, R. (PI)

STATS 315B: Modern Applied Statistics: Data Mining

Two-part sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes, in particular, regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a predictive goal, viewed from a statistical perspective as computer automated exploratory analysis of large complex data sets.
Terms: Spr | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Friedman, J. (PI)

STATS 316: Stochastic Processes on Graphs

Local weak convergence, Gibbs measures on trees, cavity method, and replica symmetry breaking. Examples include random k-satisfiability, the assignment problem, spin glasses, and neural networks. Prerequisite: 310A or equivalent.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

STATS 317: Stochastic Processes

Semimartingales, stochastic integration, Ito's formula, Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric probability. Maxima of random fields and applications to spatial statistics and imaging.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Siegmund, D. (PI)

STATS 318: Modern Markov Chains

Tools for understanding Markov chains as they arise in applications. Random walk on graphs, reversible Markov chains, Metropolis algorithm, Gibbs sampler, hybrid Monte Carlo, auxiliary variables, hit and run, Swedson-Wong algorithms, geometric theory, Poincare-Nash-Cheger-Log-Sobolov inequalities. Comparison techniques, coupling, stationary times, Harris recurrence, central limit theorems, and large deviations.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Candes, E. (PI)

STATS 319: Literature of Statistics

Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.
Terms: Aut, Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 320: Heterogeneous Data with Kernels

Mathematical and computational methods necessary to understanding analysis of heterogeneous data using generalized inner products and Kernels. For areas that need to integrate data from various sources, biology, environmental and chemical engineering, molecular biology, bioinformatics. Topics: Distances, inner products and duality. Multivariate projections. Complex heterogeneous data structures (networks, trees, categorical as well as multivariate continuous data). Canonical correlation analysis, canonical correspondence analysis. Kernel methods in Statistics. Representer theorem. Kernels on graphs. Kernel versions of standard statistical procedures. Data cubes and tensor methods.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 321: Modern Applied Statistics: Transposable Data

Topics: clustering, biclustering, and spectral clustering. Data analysis using the singular value decomposition, nonnegative decomposition, and generalizations. Plaid model, aspect model, and additive clustering. Correspondence analysis, Rasch model, and independent component analysis. Page rank, hubs, and authorities. Probabilistic latent semantic indexing. Recommender systems. Applications to genomics and information retrieval. Prerequisites: 315A,B, 305/306A,B, or consent of instructor.
Terms: Spr | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Owen, A. (PI)

STATS 322: Function Estimation in White Noise

Gaussian white noise model sequence space form. Hyperrectangles, quadratic convexity, and Pinsker's theorem. Minimax estimation on Lp balls and Besov spaces. Role of wavelets and unconditional bases. Linear and threshold estimators. Oracle inequalities. Optimal recovery and universal thresholding. Stein's unbiased risk estimator and threshold choice. Complexity penalized model selection. Connecting fast wavelet algorithms and theory. Beyond orthogonal bases.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 324: Multivariate Analysis

Classic multivariate statistics: properties of the multivariate normal distribution, determinants, volumes, projections, matrix square roots, the singular value decomposition; Wishart distributions, Hotelling's T-square; principal components, canonical correlations, Fisher's discriminant, the Cauchy projection formula.
Terms: not given this year | Units: 2-3 | Grading: Letter or Credit/No Credit

STATS 325: Multivariate Analysis and Random Matrices in Statistics

Topics on Multivariate Analysis and Random Matrices in Statistics (full description TBA)
Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Johnstone, I. (PI)

STATS 329: Large-Scale Simultaneous Inference

Estimation, testing, and prediction for microarray-like data. Modern scientific technologies, typified by microarrays and imaging devices, produce inference problems with thousands of parallel cases to consider simultaneously. Topics: empirical Bayes techniques, James-Stein estimation, large-scale simultaneous testing, false discovery rates, local fdr, proper choice of null hypothesis (theoretical, permutation, empirical nulls), power, effects of correlation on tests and estimation accuracy, prediction methods, related sets of cases ("enrichment"), effect size estimation. Theory and methods illustrated on a variety of large-scale data sets.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

STATS 330: An Introduction to Compressed Sensing (CME 362)

Compressed sensing is a new data acquisition theory asserting that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data. This revelation may change the way engineers think about signal acquisition. Course covers fundamental theoretical ideas, numerical methods in large-scale convex optimization, hardware implementations, connections with statistical estimation in high dimensions, and extensions such as recovery of data matrices from few entries (famous Netflix Prize).
Terms: given next year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 338: Topics in Biostatistics

Data monitoring and interim analysis of clinical trials. Design of Phase I, II, III trials. Survival analysis. Longitudinal data analysis.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 341: Applied Multivariate Statistics

Theory, computational aspects, and practice of a variety of important multivariate statistical tools for data analysis. Topics include classical multivariate Gaussian and undirected graphical models, graphical displays. PCA, SVD and generalizations including canonical correlation analysis, linear discriminant analysis, correspondence analysis, with focus on recent variants. Factor analysis and independent component analysis. Multidimensional scaling and its variants (e.g. Isomap, spectral clustering). Students are expected to program in R. Prerequisite: STATS 305 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hastie, T. (PI)

STATS 345: Computational Algorithms for Statistical Genetics (GENE 245, STATS 166)

Computational algorithms for human genetics research. Topics include: permutation, bootstrap, expectation maximization, hidden Markov model, and Markov chain Monte Carlo. Rationales and techniques illustrated with existing implementations commonly used in population genetics research, disease association studies, and genomics analysis. Prerequisite: GENE 244 or consent of instructor.
Terms: Spr, not given next year | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

STATS 351A: An Introduction to Random Matrix Theory (MATH 231A)

Patterns in the eigenvalue distribution of typical large matrices, which also show up in physics (energy distribution in scattering experiments), combinatorics (length of longest increasing subsequence), first passage percolation and number theory (zeros of the zeta function). Classical compact ensembles (random orthogonal matrices). The tools of determinental point processes.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

STATS 352: Spatial Statistics (STATS 253)

Statistical descriptions of spatial variability, spatial random functions, grid models, spatial partitions, spatial sampling, linear and nonlinear interpolation and smoothing with error estimation, Bayes methods and pattern simulation from posterior distributions, multivariate spatial statistics, spatial classification, nonstationary spatial statistics, space-time statistics and estimation of time trends from monitoring data, spatial point patterns, models of attraction and repulsion. Applications to earth and environmental sciences, meteorology, astronomy, remote-sensing, ecology, materials.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 355: Observational Studies (HRP 255)

This course will cover statistical methods for the design and analysis of observational studies. Topics for the course will include the potential outcomes framework for causal inference; randomized experiments; methods for controlling for observed confounders in observational studies; sensitivity analysis for hidden bias; instrumental variables; tests of hidden bias; coherence; and design of observational studies.
Terms: Win | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Baiocchi, M. (PI)

STATS 362: Monte Carlo

Random numbers and vectors: inversion, acceptance-rejection, copulas. Variance reduction: antithetics, stratification, control variates, importance sampling. MCMC: Markov chains, detailed balance, Metropolis-Hastings, random walk Metropolis, independence sampler, Gibbs sampling, slice sampler, hybrids of Gibbs and Metropolis, tempering. Sequential Monte Carlo. Quasi-Monte Carlo. Randomized quasi-Monte Carlo. Examples, problems and motivation from Bayesian statistics, machine learning, computational finance and graphics.
Terms: given next year | Units: 2-3 | Grading: Letter or Credit/No Credit

STATS 366: Modern Statistics for Modern Biology (BIOS 221)

Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate methods (PCA and extensions), sparse representations (trees, networks, contingency tables) as well as nonparametric testing (Bootstrap, permutation and Monte Carlo methods). Hands on, use R and cover many Bioconductor packages. Prerequisite: Minimal familiarity with computers. Instructor consent.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Holmes, S. (PI); Martin, T. (TA)

STATS 370: A Course in Bayesian Statistics (STATS 270)

Advanced-level Bayesian statistics. Topics: Discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. Examination of the construction of priors and the asymptotic properties of likelihoods and posterior densities. Discussion including but not limited to the case of finite dimensional parameter space. Prerequisite: familiarity with standard probability and multivariate distribution theory.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Sabatti, C. (PI)

STATS 374: Large Deviations Theory (MATH 234)

Combinatorial estimates and the method of types. Large deviation probabilities for partial sums and for empirical distributions, Cramer's and Sanov's theorems and their Markov extensions. Applications in statistics, information theory, and statistical mechanics. Prerequisite: MATH 230A or STATS 310.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Dembo, A. (PI)

STATS 375: Inference in Graphical Models

Graphical models as a unifying framework for describing the statistical relationships between large sets of variables; computing the marginal distribution of one or a few such variables. Focus is on sparse graphical structures, low-complexity algorithms, and their analysis. Topics include: variational inference; message passing algorithms; belief propagation; generalized belief propagation; survey propagation. Analysis techniques: correlation decay; distributional recursions. Applications from engineering, computer science, and statistics. Prerequisite: EE 278, STATS 116, or CS 228. Recommended: EE 376A or STATS 217.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Montanari, A. (PI)

STATS 376A: Information Theory (EE 376A)

The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178/278A or STATS 116, or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Weissman, I. (PI)

STATS 390: Consulting Workshop

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: course work in applied statistics or data analysis, and consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 396: Research Workshop in Computational Biology

Applications of Computational Statistics and Data Mining to Biological Data. Attendance mandatory. Instructor approval required.
Terms: Aut, Win, Spr | Units: 1-2 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 397: PhD Oral Exam Workshop

For Statistics PhD students defending their dissertation.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit

STATS 398: Industrial Research for Statisticians

Doctoral research as in 298, but must be conducted for an off-campus employer. Final report required. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

STATS 399: Research

Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

STATS 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

CME 100: Vector Calculus for Engineers (ENGR 154)

Computation and visualization using MATLAB. Differential vector calculus: analytic geometry in space, functions of several variables, partial derivatives, gradient, unconstrained maxima and minima, Lagrange multipliers. Integral vector calculus: multiple integrals in Cartesian, cylindrical, and spherical coordinates, line integrals, scalar potential, surface integrals, Green's, divergence, and Stokes' theorems. Examples and applications drawn from various engineering fields. Prerequisites: MATH 41 and 42, or 10 units AP credit.
Terms: Aut | Units: 5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit

CME 100A: Vector Calculus for Engineers, ACE

Students attend CME100/ENGR154 lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Enrollment by department permission only. Prerequisite: application at:http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Aut | Units: 6 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit

CME 102: Ordinary Differential Equations for Engineers (ENGR 155A)

Analytical and numerical methods for solving ordinary differential equations arising in engineering applications: Solution of initial and boundary value problems, series solutions, Laplace transforms, and non-linear equations; numerical methods for solving ordinary differential equations, accuracy of numerical methods, linear stability theory, finite differences. Introduction to MATLAB programming as a basic tool kit for computations. Problems from various engineering fields. Prerequisite: CME 100/ENGR 154 or MATH 51.
Terms: Win | Units: 5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Darve, E. (PI); Minion, M. (PI)

CME 102A: Ordinary Differential Equations for Engineers, ACE

Students attend CME102/ENGR155A lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: application at:http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Win | Units: 6 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Darve, E. (PI); Minion, M. (PI)

CME 104: Linear Algebra and Partial Differential Equations for Engineers (ENGR 155B)

Linear algebra: matrix operations, systems of algebraic equations, Gaussian elimination, undetermined and overdetermined systems, coupled systems of ordinary differential equations, eigensystem analysis, normal modes. Fourier series with applications, partial differential equations arising in science and engineering, analytical solutions of partial differential equations. Numerical methods for solution of partial differential equations: iterative techniques, stability and convergence, time advancement, implicit methods, von Neumann stability analysis. Examples and applications from various engineering fields. Prerequisite: CME 102/ENGR 155A.
Terms: Spr | Units: 5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Khayms, V. (PI)

CME 104A: Linear Algebra and Partial Differential Equations for Engineers, ACE

Students attend CME104/ENGR155B lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: application at:http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Spr | Units: 6 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Khayms, V. (PI)

CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C)

Probability: random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite: CME 100/ENGR154 or MATH 51.
Terms: Win, Sum | Units: 3-4 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit
Instructors: Khayms, V. (PI)

CME 108: Introduction to Scientific Computing

Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floating-point arithmetic, nonlinear equations, numerical solution of systems of algebraic equations, banded matrices, least squares, unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, numerical stability for time dependent problems and stiffness. Implementation of numerical methods in MATLAB programming assignments. Prerequisites: MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of CS 106A or higher).
Terms: Win, Sum | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Dunham, E. (PI); Navalpakkam Srinivasan Acharya, S. (PI)

CME 192: Introduction to MATLAB

This short course runs for the first three weeks of the quarter and is offered each quarter during the academic year. It is highly recommended for students with no prior programming experience who are expected to use MATLAB in math, science, or engineering courses. It will consist of interactive lectures and application-based assignments. The goal of the short course is to make students fluent in MATLAB and to provide familiarity with its wide array of features. The course covers an introduction of basic programming concepts, data structures, and control/flow; and an introduction to scientific computing in MATLAB, scripts, functions, visualization, simulation, efficient algorithm implementation, toolboxes, and more.
Terms: Win, Spr | Units: 1 | Grading: Satisfactory/No Credit

CME 200: Linear Algebra with Application to Engineering Computations (ME 300A)

Computer based solution of systems of algebraic equations obtained from engineering problems and eigen-system analysis, Gaussian elimination, effect of round-off error, operation counts, banded matrices arising from discretization of differential equations, ill-conditioned matrices, matrix theory, least square solution of unsolvable systems, solution of non-linear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, Cayley-Hamilton theory and function of a matrix and iterative methods. Prerequisite: familiarity with computer programming, and MATH104, 113, or equivalent.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Moin, P. (PI)

CME 204: Partial Differential Equations in Engineering (ME 300B)

Geometric interpretation of partial differential equation (PDE) characteristics; solution of first order PDEs and classification of second-order PDEs; self-similarity; separation of variables as applied to parabolic, hyperbolic, and elliptic PDEs; special functions; eigenfunction expansions; the method of characteristics. If time permits, Fourier integrals and transforms, Laplace transforms. Prerequisite: CME 200/ME 300A, equivalent, or consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lele, S. (PI)

CME 206: Introduction to Numerical Methods for Engineering (AA 214A, ME 300C)

Numerical methods from a user's point of view. Lagrange interpolation, splines. Integration: trapezoid, Romberg, Gauss, adaptive quadrature; numerical solution of ordinary differential equations: explicit and implicit methods, multistep methods, Runge-Kutta and predictor-corrector methods, boundary value problems, eigenvalue problems; systems of differential equations, stiffness. Emphasis is on analysis of numerical methods for accuracy, stability, and convergence. Introduction to numerical solutions of partial differential equations; Von Neumann stability analysis; alternating direction implicit methods and nonlinear equations. Prerequisites: CME 200/ME 300A, CME 204/ME 300B.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Iaccarino, G. (PI)

CME 211: Introduction to Programming for Scientists and Engineers (EARTHSCI 211)

Basic usage of the Python and C/C++ programming languages are introduced and used to solve representative computational problems from various science and engineering disciplines. Software design principles including time and space complexity analysis, data structures, object-oriented design, decomposition, encapsulation, and modularity are emphasized. Usage of ICME and campus wide Linux compute resources: login, file system navigation, editing files, compiling and linking, file transfer, etc. Versioning and revision control, software build utilities, and the LaTeX typesetting software are introduced and used to help complete individual programming assignments and a group project.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Legresley, P. (PI)

CME 212: Advanced Programming for Scientists and Engineers (ENERGY 212)

Advanced topics in software programming, debugging, and performance optimization are covered. The capabilities and usage of common libraries and frameworks such as BLAS, LAPACK, FFT, PETSc, and MKL/ACML are reviewed. Computer representation of integer and floating point numbers, and interoperability between C/C++ and Fortran is described. More advanced software engineering topics including: representing data in files, application checkpoint/restart, signals, unit and regression testing, and build automation. The use of debugging tools including static analysis, gdb, and Valgrind are introduced. An introduction to computer architecture covering processors, memory hierarchy, storage, and networking provides a foundation for understanding software performance. Profiles generated using gprof and OProfile, are used to help guide the performance optimization process. Computational problems from various science and engineering disciplines will be used in individual and group assignments. Prerequisites: CME 200/ME 300A; and CME 211 or CS 106X or equivalent level of programming proficiency in C/C++. Relevant courses: CS140, CS143, CS240 and EE282.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Legresley, P. (PI)

CME 213: Introduction to parallel computing using MPI, openMP, and CUDA (ME 339)

This class will give hands on experience with programming multicore processors, graphics processing units (GPU), and parallel computers. Focus will be on the message passing interface (MPI, parallel clusters) and the compute unified device architecture (CUDA, GPU). Topics will include: network topologies, modeling communication times, collective communication operations, parallel efficiency, MPI, dense linear algebra using MPI. Symmetric multiprocessing (SMP), pthreads, openMP. CUDA, combining MPI and CUDA, dense linear algebra using CUDA, sort, reduce and scan using CUDA. Pre-requisites include: C programming language and numerical algorithms (solution of differential equations, linear algebra, Fourier transforms).
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Darve, E. (PI)

CME 213B: Parallel Computing Group Projects

Students in groups of up to four will discuss, devise and implement a cluster/GPU parallel application for a discipline of mutual interest. Instructors will help guide students to relevant literature and resources. Prerequisites: Current or previous enrollment in CME 213 or equivalent background.
Terms: Spr | Units: 1 | Grading: Letter or Credit/No Credit
Instructors: Levin, S. (PI); Schreiber, R. (PI)

CME 215A: Advanced Computational Fluid Dynamics (AA 215A)

High resolution schemes for capturing shock waves and contact discontinuities; upwinding and artificial diffusion; LED and TVD concepts; alternative flow splittings; numerical shock structure. Discretization of Euler and Navier Stokes equations on unstructured meshes; the relationship between finite volume and finite element methods. Time discretization; explicit and implicit schemes; acceleration of steady state calculations; residual averaging; math grid preconditioning. Automatic design; inverse problems and aerodynamic shape optimization via adjoint methods. Pre- or corequisite: 214B or equivalent.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Jameson, A. (PI)

CME 215B: Advanced Computational Fluid Dynamics (AA 215B)

High resolution schemes for capturing shock waves and contact discontinuities; upwinding and artificial diffusion; LED and TVD concepts; alternative flow splittings; numerical shock structure. Discretization of Euler and Navier Stokes equations on unstructured meshes; the relationship between finite volume and finite element methods. Time discretization; explicit and implicit schemes; acceleration of steady state calculations; residual averaging; math grid preconditioning. Automatic design; inverse problems and aerodynamic shape optimization via adjoint methods. Pre- or corequisite: 214B or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

CME 263: Introduction to Linear Dynamical Systems (EE 263)

Applied linear algebra and linear dynamical systems with application to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: linear algebra and matrices as in MATH 103; differential equations and Laplace transforms as in EE 102A.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boyd, S. (PI)

CME 283: Startup Engineering

Spiritual sequel to Peter Thiel's CS183 course on startups. A new massively open online course (MOOC) that bridges the gap between academic computer science and production software engineering. Fast-paced introduction to key tools and techniques (command line, dotfiles, text editor, distributed version control, debugging, testing, documentation, reading code, deployments), featuring guest appearances by senior engineers from successful startups and large-scale academic projects. Over the course of the class, students will build a command line application, expose it as a web service, and then link other students' applications and services together to build an HTML5 mobile app. General principles are illustrated through modern Javascript and the latest web technologies, including Node, Backbone, Coffeescript, Bootstrap, Git, and Github. Prerequisites: Basic computer science as per CS106B. Recommended: some familiarity with HTML, CSS, and Javascript.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pande, V. (PI); Srinivasan, B. (PI)

CME 291: Master's Research

Students require faculty sponsor. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit | Grading: Letter or Credit/No Credit

CME 300: Departmental Seminar Series

Required for first-year ICME Ph.D. students; recommended for first-year ICME M.S. students. Presentations about research at Stanford by faculty and researchers from Engineering, H&S, and organizations external to Stanford. May be repeated for credit.
Terms: Aut, Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Murray, W. (PI)

CME 302: Numerical Linear Algebra

First in a three quarter graduate sequence. Solution of systems of linear equations: direct methods, error analysis, structured matrices; iterative methods and least squares. Parallel techniques. Prerequisites: CME 108, MATH 103 or 113.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CME 303: Partial Differential Equations of Applied Mathematics (MATH 220)

First-order partial differential equations; method of characteristics; weak solutions; elliptic, parabolic, and hyperbolic equations; Fourier transform; Fourier series; and eigenvalue problems. Prerequisite: foundation in multivariable calculus and ordinary differential equations.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Vasy, A. (PI)

CME 304: Numerical Optimization (MS&E 315)

Solution of nonlinear equations; unconstrained optimization; linear programming; quadratic programming; global optimization; general linearly and nonlinearly constrained optimization. Theory and algorithms to solve these problems. Prerequisite: background in analysis and numerical linear algebra.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Murray, W. (PI)

CME 305: Discrete Mathematics and Algorithms (MS&E 316)

Topics: enumeration such as Cayley's theorem and Prufer codes, SDR, flows and cuts (deterministic and randomized algorithms), probabilistic methods and random graphs, asymptotics (NP-hardness and approximation algorithms). Topics illustrated with EE, CS, and bioinformatics applications. Prerequisites: MATH 51 or 103 or equivalents.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

CME 306: Numerical Solution of Partial Differential Equations (MATH 226)

Hyperbolic partial differential equations: stability, convergence and qualitative properties; nonlinear hyperbolic equations and systems; combined solution methods from elliptic, parabolic, and hyperbolic problems. Examples include: Burger's equation, Euler equations for compressible flow, Navier-Stokes equations for incompressible flow. Prerequisites: MATH 220A or CME 302.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

CME 308: Stochastic Methods in Engineering (MATH 228)

Review of basic probability; Monte Carlo simulation; state space models and time series; parameter estimation, prediction, and filtering; Markov chains and processes; stochastic control; and stochastic differential equations. Examples from various engineering disciplines. Prerequisites: exposure to probability; background in real variables and analysis.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Papanicolaou, G. (PI)

CME 309: Randomized Algorithms (CS 365)

Design and analysis of algorithms that use randomness to guide their computations. Topics include: basic tools, from probability theory and probabilistic analysis that are recurrent in algorithmic applications; randomized complexity theory and game-theoretic techniques; algebraic techniques, probability amplification and derandomization. Applications: sorting and searching, data structures, combinatorial optimization and graph algorithms, geometric algorithms and linear programming, approximation and counting problems, similarity search and metric embeddings, online algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Goel, A. (PI)

CME 321A: Mathematical Methods of Imaging (MATH 221A)

Image denoising and deblurring with optimization and partial differential equations methods. Imaging functionals based on total variation and l-1 minimization. Fast algorithms and their implementation.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ryzhik, L. (PI)

CME 321B: Mathematical Methods of Imaging (MATH 221B)

Array imaging using Kirchhoff migration and beamforming, resolution theory for broad and narrow band array imaging in homogeneous media, topics in high-frequency, variable background imaging with velocity estimation, interferometric imaging methods, the role of noise and inhomogeneities, and variational problems that arise in optimizing the performance of array imaging algorithms.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Papanicolaou, G. (PI)

CME 322: Spectral Methods in Computational Physics (ME 408)

Data analysis, spectra and correlations, sampling theorem, nonperiodic data, and windowing; spectral methods for numerical solution of partial differential equations; accuracy and computational cost; fast Fourier transform, Galerkin, collocation, and Tau methods; spectral and pseudospectral methods based on Fourier series and eigenfunctions of singular Sturm-Liouville problems; Chebyshev, Legendre, and Laguerre representations; convergence of eigenfunction expansions; discontinuities and Gibbs phenomenon; aliasing errors and control; efficient implementation of spectral methods; spectral methods for complicated domains; time differencing and numerical stability.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Moin, P. (PI)

CME 325: Numerical Approximations of Partial Differential Equations in Theory and Practice

Finite volume and finite difference methods for initial boundary value problems in multiple space dimensions. Emphasis is on formulation of boundary conditions for the continuous and the discrete problems. Analysis of numerical methods with respect to stability, accuracy, and error behavior. Techniques of treating non-rectangular domains, and effects of non-regular grids.
Terms: not given this year | Units: 1-2 | Grading: Letter or Credit/No Credit

CME 326: Numerical Methods for Initial Boundary Value Problems

Initial boundary value problems model many phenomena in engineering and science such as, fluid flow problems, wave propagation, fluid-structure interaction, conjugate heat transfer and financial mathematics. We discuss numerical techniques for such simulations and focus on the underlying principles and theoretical understanding. Emphasis is on stability, convergence and efficiency for methods applied to hyperbolic and parabolic initial boundary value problems.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CME 327: Numerical Methods for Stiff Problems

Focus is on analysis of numerical techniques for stiff ordinary differential equations, including those resulting from spatial discretization of partial differential equations. Topics include stiffness, convergence, stability, adaptive time stepping, implicit time-stepping methods (SDIRK, Rosenbrock), linear and nonlinear system solvers (Fixed Point, Newton, Multigrid, Krylov subspace methods) and preconditioning. Pre-requisites: CME200/ME300A or equivalent; or consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CME 328: Advanced Topics in Partial Differential Equations

Contents change each time and is taught as a topics course, most likely by a faculty member visiting from another institution. May be repeated for credit. Topic in 2012-13: numerical solution of time-dependent partial differential equations is a fundamental tool for modeling and prediction in many areas of science and engineering. In this course we explore the stability, accuracy, efficiency, and appropriateness of specialized temporal integration strategies for different classes of partial differential equations including stiff problems and fully implicit methods, operator splitting and semi-implicit methods, extrapolation methods, multirate time integration, multi-physics problems, symplectic integration, and temporal parallelism. Prerequisites: recommended CME303 and 306 or with instructor's consent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Minion, M. (PI)

CME 330: Applied Mathematics in the Chemical and Biological Sciences (CHEMENG 300)

Mathematical solution methods via applied problems including chemical reaction sequences, mass and heat transfer in chemical reactors, quantum mechanics, fluid mechanics of reacting systems, and chromatography. Topics include generalized vector space theory, linear operator theory with eigenvalue methods, phase plane methods, perturbation theory (regular and singular), solution of parabolic and elliptic partial differential equations, and transform methods (Laplace and Fourier). Prerequisites: CME 102/ENGR 155A and CME 104/ENGR 155B, or equivalents.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Shaqfeh, E. (PI)

CME 334: Advanced Methods in Numerical Optimization (MS&E 312)

Topics include interior-point methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. May be repeated for credit.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Murray, W. (PI)

CME 336: Linear and Conic Optimization with Applications (MS&E 314)

Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Algorithms include the interior-point, barrier function, and cutting plane methods. Related convex analysis, including the separating hyperplane theorem, Farkas lemma, dual cones, optimality conditions, and conic inequalities. Complexity and/or computation efficiency analysis. Applications to combinatorial optimization, sensor network localization, support vector machine, and graph realization. Prerequisite: MS&E 211 or equivalent.
Terms: Win, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit

CME 338: Large-Scale Numerical Optimization (MS&E 318)

The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basic factorization and updates. Interior methods. The reduced-gradient method, augmented Lagrangian methods, and SQP methods. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315; CME 108, 200, 302, 304, 334, or 335.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Saunders, M. (PI)

CME 342: Parallel Methods in Numerical Analysis

Emphasis is on techniques for obtaining maximum parallelism in numerical algorithms, especially those occurring when solving matrix problems, partial differential equations, and the subsequent mapping onto the computer. Implementation issues on parallel computers. Topics: parallel architecture, programming models (MPI, GPU Computing with CUDA ¿ quick review), matrix computations, FFT, fast multiple methods, domain decomposition, graph partitioning, discrete algorithms. Prerequisites: 302 or 200 (ME 300A), 213 or equivalent, or consent of instructor. Recommended: differential equations and knowledge of a high-level programming language such as C or C++ (F90/95 also allowable).
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

CME 345: Model Reduction

Model reduction is an indispensable tool for computational-based design and optimization, statistical analysis, embedded computing and real-time optimal control. This course presents the basic mathematical theory for projection-based model reduction. Topics include: notions of linear dynamical systems and projection; projection-based model reduction; error analysis; proper orthogonal decomposition; Hankel operator and balancing of a linear dynamical system; balanced truncation method: modal truncation and other reduction methods for linear oscillators; model reduction via moment matching methods based on Krylov subspaces; introduction to model reduction of parametric systems and notions of nonlinear model reduction. Course material is complemented by a balanced set of theoretical, algorithmic and Matlab computer programming assignments. Pre-requisites: CME200 or equivalent, CME 263 or equivalent and basic numerical methods for ODEs.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Farhat, C. (PI)

CME 356: Engineering Functional Analysis and Finite Elements (ME 412)

Concepts in functional analysis to understand models and methods used in simulation and design. Topology, measure, and integration theory to introduce Sobolev spaces. Convergence analysis of finite elements for the generalized Poisson problem. Extensions to convection-diffusion-reaction equations and elasticity. Upwinding. Mixed methods and LBB conditions. Analysis of nonlinear and evolution problems. Prerequisites: 335A,B, CME 200, CME 204, or consent of instructor. Recommended: 333, MATH 171.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lew, A. (PI)

CME 358: Finite Element Method for Fluid Mechanics

Mathematical theory of the finite element method for incompressible flows; related computational algorithms and implementation details. Poisson equation; finite element method for simple elliptic problems; notions of mathematical analysis of non-coercive partial differential equations; the inf-sup or Babushka-Brezzi condition and its applications to the Stokes and Darcy problems; presentation of stable mixed finite element methods and corresponding algebraic solvers; stabilization approaches in the context of advection-diffusion equation; numerical solution of the incompressible Navier-Stokes equations by finite element method. Theoretical, computational, and MATLAB computer programming assignments. Prerequisites: foundation in multivariate calculus and ME 335A or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CME 362: An Introduction to Compressed Sensing (STATS 330)

Compressed sensing is a new data acquisition theory asserting that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data. This revelation may change the way engineers think about signal acquisition. Course covers fundamental theoretical ideas, numerical methods in large-scale convex optimization, hardware implementations, connections with statistical estimation in high dimensions, and extensions such as recovery of data matrices from few entries (famous Netflix Prize).
Terms: given next year | Units: 3 | Grading: Letter or Credit/No Credit

CME 364A: Convex Optimization I (CS 334A, EE 364A)

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, EE178/278A.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boyd, S. (PI)

CME 364B: Convex Optimization II (EE 364B)

Continuation of 364. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications. Substantial project. Prerequisite: 364A.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CME 390: Curricular Practical Training

May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Murray, W. (PI)

CME 399: Special Research Topics in Computational and Mathematical Engineering

Graduate-level research work not related to report, thesis, or dissertation. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter or Credit/No Credit

CME 400: Ph.D. Research

Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit

CME 444: Computational Consulting

Advice by graduate students under supervision of ICME faculty. Weekly briefings with faculty adviser and associated faculty to discuss ongoing consultancy projects and evaluate solutions. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Gerritsen, M. (PI)

CME 500: Numerical Analysis and Computational and Mathematical Engineering Seminar

Weekly research lectures by experts from academia, national laboratories, industry, and doctoral students. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Gerritsen, M. (PI)

CME 510: Linear Algebra and Optimization Seminar

Recent developments in numerical linear algebra and numerical optimization. Guest speakers from other institutions and local industry. Goal is to bring together scientists from different theoretical and application fields to solve complex scientific computing problems. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Saunders, M. (PI)

CME 520: Topics in Simu Hum Phys & Ana Sys

Biweekly interdisciplinary lecture series on the development of computational tools for modeling and simulation of human physiological and anatomical systems. Lectures by instructors and guest speakers on topics such as surgical simulation, anatomical & surgical Modeling, neurological Systems, and biomedical models of human movement. Group discussions, team based assignments, and project work. Prerequisite: Medical students, residents or fellows from school of medicine, and computationally oriented students with a strong interest to explore computational and mathematical methods related to the health sciences.
Terms: Win | Units: 1 | Repeatable for credit | Grading: Medical Satisfactory/No Credit
Instructors: Gerritsen, M. (PI); Navalpakkam Srinivasan Acharya, S. (PI)

CME 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

CME 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

MS&E 22Q: The Flaw of Averages

Uncertain assumptions in business and public policy are often replaced with single ¿best guess¿ or average numbers. This leads to a fallacy as fundamental as the belief that the earth is flat, which I call the Flaw of Averages. It states, in effect, that: plans based on average assumptions are wrong on average. This class will discuss mitigations of the flaw of averages using simulation and other methods from probability management.
Terms: Aut | Units: 3 | Grading: Satisfactory/No Credit
Instructors: Savage, S. (PI)

MS&E 41: Financial Literacy

Practical knowledge about personal finance and money management including budgeting, pay checks, credit cards, banking, insurance, taxes, and saving. Class especially appropriate for those soon to be self-supporting. Limited enrollment. Admission by order of enrollment in Axess.
Terms: Win, Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Morrison, M. (PI)

MS&E 52: Introduction to Decision Making

Experienced management consultants share lessons and war stories. Case studies, disguised examples from real engagements, and movie clips illustrate theories and concepts of decision analysis. Student teams critique decisions made in actual organizations. Topics include what makes a good decision, how decisions can be made better, framing and structuring techniques, modeling and analysis tools, biases and probability assessment, evaluation and appraisal methods, decision psychology, creativity and organizational leadership, and effective presentation styles. Not intended for MS&E majors.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 71SI: Entrepreneurship through the Lens of Venture Capital

How successful startups navigate funding, managing, and scaling their new enterprise. Process explored through guest lectures and mentorship from experienced venture capital investors and seasoned entrepreneurs who manage these issues on a daily basis in Silicon Valley. Course themes: customer value equation, board management, market strategy, company culture, and hyper growth. Enrollment is limited to 20 students. Visit http://www.stanford.edu/dept/MSandE/lensofvc for application and more information.
Terms: Win | Units: 1-2 | Grading: Satisfactory/No Credit
Instructors: Kosnik, T. (PI)

MS&E 92Q: International Environmental Policy

Preference to sophomores. Science, economics, and politics of international environmental policy. Current negotiations on global climate change, including actors and potential solutions. Sources include briefing materials used in international negotiations and the U.S. Congress.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Weyant, J. (PI)

MS&E 101: Undergraduate Directed Study

Subject of mutual interest to student and faculty member. Prerequisite: faculty sponsor.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 107: Interactive Management Science (MS&E 207)

Analytical techniques such as linear and integer programming, Monte Carlo simulation, forecasting, decision analysis, and Markov chains in the environment of the spreadsheet. Probability management. Materials include spreadsheet add-ins for implementing these and other techniques. Emphasis is on building intuition through interactive modeling, and extending the applicability of this type of analysis through integration with existing business data structures.
Terms: Aut, Sum | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Savage, S. (PI)

MS&E 108: Senior Project

Restricted to MS&E majors in their senior year. Students carry out a major project in groups of four, applying techniques and concepts learned in the major. Project work includes problem identification and definition, data collection and synthesis, modeling, development of feasible solutions, and presentation of results. Service Learning Course (certified by Haas Center).
Terms: Win | Units: 5 | Grading: Letter (ABCD/NP)

MS&E 111: Introduction to Optimization (ENGR 62)

Formulation and analysis of linear optimization problems. Solution using Excel solver. Polyhedral geometry and duality theory. Applications to contingent claims analysis, production scheduling, pattern recognition, two-player zero-sum games, and network flows. Prerequisite: MATH 51.
Terms: Aut, Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Goel, A. (PI)

MS&E 112: Mathematical Programming and Combinatorial Optimization (MS&E 212)

Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: CS 106A or X; ENGR 62 or MATH 103.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

MS&E 120: Probabilistic Analysis

Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, and physical problems. Spreadsheets illustrate and solve problems as a complement to analytical closed-form solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: MATH 51. Recommended: knowledge of spreadsheets.
Terms: Aut | Units: 5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Shachter, R. (PI)

MS&E 121: Introduction to Stochastic Modeling

Stochastic processes and models in operations research. Discrete and continuous time parameter Markov chains. Queuing theory, inventory theory, simulation. Prerequisite: 120 or Statistics 116.
Terms: Win | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Glynn, P. (PI)

MS&E 130: Information Networks and Services

Architecture of the Internet and performance engineering of computer systems and networks. Switching, routing and shortest path algorithms. Congestion management and queueing networks. Peer-to-peer networking. Wireless and mobile networking. Information service engineering and management. Search engines and recommendation systems. Reputation systems and social networking technologies. Security and trust. Information markets. Select special topics and case studies. Prerequisites: 111, 120, and CS 106A.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Bambos, N. (PI)

MS&E 134: Organization Change and Information Systems (MS&E 234)

Leading organizational change and Information Systems. Case method discussions and lectures. Themes include: real-time enterprise; reengineering; organization transformation, cross-functional teams, IT development, and leading IT. Course includes a group project that is defined and approved during the first two weeks of class. Limited enrollment. Prerequisites: CS 106A, 180, or equivalents.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

MS&E 140: Accounting for Managers and Entrepreneurs (MS&E 240)

Non-majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making. Designed for the user of accounting information and not as an introduction to a professional accounting career. Enrollment limited. Admission by order of enrollment.
Terms: Aut, Win, Sum | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Stanton, F. (PI)

MS&E 142: Introductory Financial Analysis

Evaluation and management of money, complicated by temporary distributions and uncertainty. The ¿time-value of money" and its impact on economic decisions (both personal and corporate) with the introduction of interest rate (constant or varying over time); several approaches critically examined and made consistent as suitable metrics of comparison. The concept of investment diversification in the presence of uncertainty; portfolio selection and efficient frontier analysis leading to the formulation of the Capital Asset Pricing Model; practical implementation of the concepts, including comparison of loan (e.g., house and auto) terms, credit card financial terms, interest rate term structure and its relationship to rate-of-return analysis, and graphical presentation of uncertain investment alternatives; and current economic news of interest. Critical thinking, discussion, and interaction, using group and computer labs assignments. Prerequisites: differential calculus and probability. Recommended: optimization.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Chiu, S. (PI)

MS&E 146: Corporate Financial Management

Key functions of finance in both large and small companies, and the core concepts and key analytic tools that provide their foundation. Making financing decisions, evaluating investments, and managing cashflow, profitability and risk. Designing performance metrics to effectively measure and align the activities of functional groups and individuals within the firm. Structuring relationships with key customers, partners and suppliers. Prerequisite: 142 or 245G or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Johnson, B. (PI)

MS&E 152: Introduction to Decision Analysis (MS&E 152W)

How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude. Students seeking to fulfill the Writing in the Major requirement should register for MS&E 152W.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Shachter, R. (PI)

MS&E 152W: Introduction to Decision Analysis (MS&E 152)

How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude. Students seeking to fulfill the Writing in the Major requirement should register for MS&E 152W.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Shachter, R. (PI)

MS&E 175: Innovation, Creativity, and Change

Problem solving in organizations; creativity and innovation skills; thinking tools; creative organizations, teams, individuals, and communities. Limited enrollment. (Katila)
Terms: alternate years, given next year | Units: 3-4 | Grading: Letter (ABCD/NP)

MS&E 178: The Spirit of Entrepreneurship

Is there more to entrepreneurship than inventing the better mouse trap? This course uses the speakers from the Entrepreneurial Thought Leader seminar (MS&E472) to drive research and discussion about what makes an entrepreneur successful. Topics include venture financing, business models, and interpersonal dynamics in the startup environment. Students meet before and after MS&E 472 to prepare for and debrief after the sessions. Enrollment limited to 50 students. Admission by application.
Terms: Aut, Win, Spr | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Belani, R. (PI); Corey, T. (PI); Roizen, H. (PI)

MS&E 180: Organizations: Theory and Management

For undergraduates only; preference to MS&E majors. Classical and contemporary organization theory; the behavior of individuals, groups, and organizations. Limited enrollment. Admission by application. Students must attend first session.
Terms: Aut, Spr | Units: 4 | Grading: Letter (ABCD/NP)

MS&E 181: Issues in Technology and Work for a Postindustrial Economy

How changes in technology and organization are altering work and lives. Approaches to studying and designing work. How understanding work and work practices can assist engineers in designing better technologies and organizations. Topics include job design, distributed and virtual organizations, the blurring of boundaries between work and family life, computer supported cooperative work, trends in skill requirements and occupational structures, monitoring and surveillance in the workplace, downsizing and its effects on work systems, project work and project-based lifestyles, the growth of contingent employment, telecommuting, electronic commerce, and the changing nature of labor relations. Enrollment limited to 50 students. Preference to MS&E, STS, and CEE seniors, followed by MS&E, STS, and CEE juniors.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Barley, S. (PI)

MS&E 185: Global Work

Issues, challenges, and opportunities facing workers, teams, and organizations working across national boundaries. Topics include geographic distance, time zones, language and cultural differences, technologies to support distant collaboration, team dynamics, and corporate strategy. Limited enrollment. Admission by application.
Terms: Win, Spr | Units: 4 | Grading: Letter (ABCD/NP)

MS&E 189: Social Networks - Theory, Methods, and Applications

Introduces students to the theoretical, substantive, and methodological foundations of social networks. The social network paradigm seeks to explain how social relations facilitate and constrain an actor¿s opportunities, behaviors, and cognitions. Topics include: network concepts and principles; network data collection, measurement, and analysis; and applications in management, engineering, and related disciplines.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Lifschitz, A. (PI)

MS&E 190: Methods and Models for Policy and Strategy Analysis

Guest lectures by departmental practitioners. Emphasis is on links among theory, application, and observation. Environmental, national security, and health policy; marketing, new technology, and new business strategy analyses. Comparisons between domains and methods.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Negoescu, D. (PI)

MS&E 193: Technology and National Security (MS&E 193W, MS&E 293)

The interaction of technology and national security policy from the perspective of history to implications for the new security imperative, homeland defense. Key technologies in nuclear and biological weapons, military platforms, and intelligence gathering. Policy issues from the point of view of U.S. and other nations. The impact of terrorist threat. Guest lecturers include key participants in the development of technology and/or policy. Students seeking to fulfill the WIM requirement should register for 193W.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 193W: Technology and National Security (MS&E 193, MS&E 293)

The interaction of technology and national security policy from the perspective of history to implications for the new security imperative, homeland defense. Key technologies in nuclear and biological weapons, military platforms, and intelligence gathering. Policy issues from the point of view of U.S. and other nations. The impact of terrorist threat. Guest lecturers include key participants in the development of technology and/or policy. Students seeking to fulfill the WIM requirement should register for 193W.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 197: Ethics and Public Policy (PUBLPOL 103B, STS 110)

Ethical issues in science- and technology-related public policy conflicts. Focus is on complex, value-laden policy disputes. Topics: the nature of ethics and morality; rationales for liberty, justice, and human rights; and the use and abuse of these concepts in policy disputes. Case studies from biomedicine, environmental affairs, technical professions, communications, and international relations.
Terms: not given this year | Units: 5 | UG Reqs: GER:ECEthicReas, GER:DBHum | Grading: Letter (ABCD/NP)

MS&E 201: Dynamic Systems

Goal is to think dynamically in decision making, and recognize and analyze dynamic phenomena in diverse situations. Concepts: formulation and analysis; state-space formulation; solutions of linear dynamic systems, equilibria, dynamic diagrams; eigenvalues and eigenvectors of linear systems, the concept of feedback; nonlinear dynamics, phase plane analysis, linearized analysis, Liapunov functions, catastrophe theory. Examples: grabber-holder dynamics, technology innovation dynamics, creation of new game dynamics in business competition, ecosystem dynamics, social dynamics, and stochastic exchange dynamics. Prerequisite: MATH 51 or equivalent.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Tse, E. (PI)

MS&E 206: Art of Mathematical Modeling

Practicum. Students build mathematical models of real-life, ill-framed problems. Emphasis is on framing the issues, articulating modeling components logically (drawing from student's mathematical background), and analyzing the resulting model. Hands-on modeling. Project work in small groups. Prerequisites: basic analysis, calculus and algebra, and probability theory. Recommended: decision analysis, optimization and dynamic systems.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Kieffel, H. (PI)

MS&E 207: Interactive Management Science (MS&E 107)

Analytical techniques such as linear and integer programming, Monte Carlo simulation, forecasting, decision analysis, and Markov chains in the environment of the spreadsheet. Probability management. Materials include spreadsheet add-ins for implementing these and other techniques. Emphasis is on building intuition through interactive modeling, and extending the applicability of this type of analysis through integration with existing business data structures.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Savage, S. (PI)

MS&E 208A: Practical Training

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a one-page statement showing relevance to degree program along with offer letter before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Master's students are limited to one quarter of practical training. B.S. and Ph.D. students may take each of A, B, and C once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit

MS&E 208B: Practical Training

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a one-page statement showing relevance to degree program along with offer letter before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Master's students are limited to one quarter of practical training. B.S. and Ph.D. students may take each of A, B, and C once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit

MS&E 208C: Practical Training

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a one-page statement showing relevance to degree program along with offer letter before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Master's students are limited to one quarter of practical training. B.S. and Ph.D. students may take each of A, B, and C once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit

MS&E 211: Linear and Nonlinear Optimization

Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: MATH 51.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Ye, Y. (PI)

MS&E 212: Mathematical Programming and Combinatorial Optimization (MS&E 112)

Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: CS 106A or X; ENGR 62 or MATH 103.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 220: Probabilistic Analysis

Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: the structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, and physical problems. Spreadsheets illustrate and solve problems as a complement to analytical closed-form solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: MATH 51. Recommended: knowledge of spreadsheets.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Chiu, S. (PI)

MS&E 221: Stochastic Modeling

Focus is on time-dependent random phenomena. Topics: discrete and continuous time Markov chains, renewal processes, queueing theory, and applications. Emphasis is on building a framework to formulate and analyze probabilistic systems. Prerequisite: 220 or consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Johari, R. (PI)

MS&E 223: Simulation

Discrete-event systems, generation of uniform and non-uniform random numbers, Monte Carlo methods, programming techniques for simulation, statistical analysis of simulation output, efficiency-improvement techniques, decision making using simulation, applications to systems in computer science, engineering, finance, and operations research. Prerequisites: working knowledge of a programming language such as C, C++, Java, or FORTRAN; probability; and statistical methods.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Haas, P. (PI)

MS&E 233: Networked Markets

An introduction to economic analysis for modern online services and systems. Topics include: Examples of networked markets. Online advertising. Recommendation and reputation systems. Pricing digital media. Network effects and network externalities. Social learning and herd behavior. Markets and information. Prerequisites: Math 51and probability at the level of MS&E 220 or equivalent. No prior economics background will be assumed; requisite concepts will be introduced as needed.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Goel, A. (PI)

MS&E 234: Organization Change and Information Systems (MS&E 134)

Leading organizational change and Information Systems. Case method discussions and lectures. Themes include: real-time enterprise; reengineering; organization transformation, cross-functional teams, IT development, and leading IT. Course includes a group project that is defined and approved during the first two weeks of class. Limited enrollment. Prerequisites: CS 106A, 180, or equivalents.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

MS&E 236: Game Theory with Engineering Applications

Strategic interactions among multiple decision makers emphasizing applications to engineering systems. Topics: efficiency and fairness; collective decision making and cooperative games; static and dynamic noncooperative games; and complete and incomplete information models. Competition: Bertrand, Cournot, and Stackelberg models. Mechanism design: auctions, contracts. Examples from engineering problems. Prerequisites: MATH 51 and exposure to probability such as 120 or EE 178. Recommended: 211, concurrent enrollment in 241 or ECON 202.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 236H: Game Theory with Engineering Applications

Advanced and mathematically more rigorous version of MS&E 236. Strategic interactions among multiple decision makers emphasizing applications to engineering systems. Topics: efficiency and fairness; collective decision making and cooperative games; static and dynamic noncooperative games; and complete and incomplete information models. Competition: efficient markets; Bertrand, Cournot, and Stackelberg models. Mechanism design: auctions, contracts. Examples from engineering problems. Prerequisites: mathematical maturity; MATH 51; probability at the level of 220, STATS 116, or equivalent. Recommended: 211, concurrent enrollment in 241 or ECON 202.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 237: The Social Data Revolution: Data Mining and Electronic Business

Hands-on exploration of current and emergent data sources and their impact on individuals, business and society: recommendation engines, reputation systems, social network analysis, and engagement metrics. Guest speakers, homework assignments and group projects (e.g., Twitter and Facebook apps) combine data strategy, machine learning, modern and traditional marketing, behavioral economics, and incentive design. Cases include Amazon.com, BestBuy, MySpace, Lufthansa, and startups. Prerequisites: intellectual curiosity, entrepreneurial spirit, some programming experience (details at weigend.com/teaching), and willingness to implement in the real world.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 238: Leading Trends in Information Technology

Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e.g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Virtualization, Cloud Computing, Security, Mobility and Unified Communications.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Barreto, D. (PI)

MS&E 238A: Leading Trends in Information Technology

Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e.g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Virtualization, Cloud Computing, Security, Mobility and Unified Communications.
Terms: Sum | Units: 1 | Grading: Satisfactory/No Credit

MS&E 239: Computational Advertising

Computational, economic, and optimization issues in online advertising, in contexts including web search, social networks, web surfing, and online multimedia. Overview of scientific and engineering issues arising in building online advertising platforms for Internet advertising formats, as well as ad pricing, ad auctions, and ad optimization. Research frontiers of this young discipline. Limited enrollment. Prerequisites: elementary probability and linear algebra.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 240: Accounting for Managers and Entrepreneurs (MS&E 140)

Non-majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making. Designed for the user of accounting information and not as an introduction to a professional accounting career. Enrollment limited. Admission by order of enrollment.
Terms: Aut, Win, Sum | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Stanton, F. (PI)

MS&E 241: Economic Analysis

Principal methods of economic analysis of the production activities of firms, including production technologies, cost and profit, and perfect and imperfect competition; individual choice, including preferences and demand; and the market-based system, including price formation, efficiency, and welfare. Practical applications of the methods presented. See 341 for continuation of 241. Recommended: 211, ECON 50.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Sweeney, J. (PI)

MS&E 242: Investment Science

Theory and application of modern quantitative investment analysis from an engineering perspective. How investment concepts are used to evaluate and manage opportunities, portfolios, and investment products including stocks, bonds, mortgages, and annuities. Topics: deterministic cash flows (term structure of interest rates, bond portfolio immunization, project optimization); mean-variance theory (Markowitz model, capital asset pricing); and arbitrage pricing theory. Group project. Prerequisites: 120, MATH 51, or equivalents. Recommended: 111, 140, knowledge of spreadsheets. Limited enrollment.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Infanger, G. (PI)

MS&E 242H: Investment Science Honors

Concepts of modern quantitative finance and investments. Basic concepts under certainty including arbitrage, term structure of interest rates, and bond portfolio immunization. A situation of uncertainty in one period. Topics: arbitrage; theorems of asset pricing; pricing measures; derivative securities; applications and estimating of financial risk measures; mean-variance portfolio analysis; and equilibrium and the capital asset pricing model. Group projects involving financial market data. Enrollment limited. Prerequisites: basic probability, statistics, and economics such as MS&E 120, 121, MATH 51, or equivalents. No prior knowledge of finance required.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Giesecke, K. (PI)

MS&E 242S: Investment Science

Emphasis is on a cash flow approach. Topics include deterministic cash flow analysis (time value of money, present value, internal rate of return, taxes, inflation), fixed income securities, duration and bond portfolio immunization, term structure of interest rates (spot rates, discount factors, forward rates), Fisher-Weill duration and immunization, capital budgeting, dynamic optimization problems, investments under uncertainty, mean-variance portfolio theory, capital asset pricing, and basic options theory. Goal is to create a link between engineering analysis and business decision making.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 243: Energy and Environmental Policy Analysis

Concepts, methods, and applications. Energy/environmental policy issues such as automobile fuel economy regulation, global climate change, research and development policy, and environmental benefit assessment. Group project. Prerequisite: MS&E 241 or ECON 50, 51.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Sweeney, J. (PI)

MS&E 245G: Finance for Non-MBAs (ECON 135)

For graduate students and advanced undergraduates. The foundations of finance; applications in corporate finance and investment management. Financial decisions made by corporate managers and investors with focus on process valuation. Topics include criteria for investment decisions, valuation of financial assets and liabilities, relationships between risk and return, market efficiency, and the valuation of derivative securities. Corporate financial instruments including debt, equity, and convertible securities. Equivalent to core MBA finance course, FINANCE 220. Prerequisites: ECON 51, or ENGR 60, or equivalent; ability to use spreadsheets, and basic probability and statistics concepts including random variables, expected value, variance, covariance, and simple estimation and regression.
Terms: Aut | Units: 3-5 | Grading: Letter or Credit/No Credit
Instructors: Admati, A. (PI)

MS&E 247G: International Financial Management

With a daily volume of more than $1.8tr the foreign exchange market is by far the largest financial market in the world. It is also one of the most important ones as it is impossible to avoid exchange rate risk in the global economy. We will examine various aspects of the foreign exchange market. First, we will examine the role of governments and central banks. We will then focus on the markets for spot exchange, currency forwards, options, swaps, international bonds, and international equities. For each of these markets, the valuation of instruments traded in these markets and, through cases, the application of these instruments to managing exposure to exchange rates, financing in international capital markets, and international capital budgeting. It is strongly recommended that students take Finance for Non-MBAs (FINANCE 221/MS&E 245G/ECON 135) as a pre- or co-requisite to this course. MS&E 242/242S/242H or MATH 238/STATS 250 are also acceptable.
Terms: not given this year | Units: 4 | Grading: Letter (ABCD/NP)

MS&E 247S: International Investments

International financial markets, their comparative behavior and interrelations. Focus is on assets traded in liquid markets: currencies, equities, bonds, swaps, and derivatives. Topics: institutional arrangements, taxation and regulation, international arbitrage and parity conditions, valuation of target firms for cross-border acquisitions, direct foreign investment, international diversification and portfolio management, derivative instruments and dynamic investment strategies, international performance analysis, international capital flows and financial crises, and topics of current relevance and importance.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 248: Economics of Natural Resources

Intertemporal economic analysis of natural resource use, particularly energy, and including air, water, and other depletable mineral and biological resources. Emphasis is on an integrating theory for depletable and renewable resources. Stock-flow relationships; optimal choices over time; short- and long-run equilibrium conditions; depletion/extinction conditions; market failure mechanisms (common-property, public goods, discount rate distortions, rule-of-capture); policy options. Prerequisite: 241 or ECON 51.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

MS&E 249: Economic Growth and Development

What generates economic growth. Emphasis is on theory accompanied by intuition, illustrated with country cases. Topics: the equation of motion of an economy; optimal growth theory; calculus of variations and optimal control approaches; deriving the Euler and Pontriaguine equations from economic reasoning. Applications: former planned economies in Russia and E. Europe; the present global crisis: causes and consequences; a comparative study of India and China. The links between economic growth and civilization; the causes of the rise and decline of civilizations; lessons for the future. Intended for graduate students. Prerequisite: multivariable calculus.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 250A: Engineering Risk Analysis

The techniques of analysis of engineering systems for risk management decisions involving trade-offs (technical, human, environmental aspects). Elements of decision analysis; probabilistic risk analysis (fault trees, event trees, systems dynamics); economic analysis of failure consequences (human safety and long-term economic discounting); and case studies such as space systems, nuclear power plants, and medical systems. Public and private sectors. Prerequisites: probability, decision analysis, stochastic processes, and convex optimization.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pate-Cornell, M. (PI)

MS&E 250B: Project Course in Engineering Risk Analysis

Students, individually or in groups, choose, define, formulate, and resolve a real risk management problem, preferably from a local firm or institution. Oral presentation and report required. Scope of the project is adapted to the number of students involved. Three phases: risk assessment, communication, and management. Emphasis is on the use of probability for the treatment of uncertainties and sensitivity to problem boundaries. Limited enrollment. Prerequisites: MS&E 250A and consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pate-Cornell, M. (PI)

MS&E 251: Stochastic Decision Models (EE 365)

Efficient formulation and computational solution of sequential decision problems under uncertainty. Markov decision chains and stochastic programming. Maximum expected present value and rate of return. Optimality of simple policies: myopic, linear, index, acceptance limit, and (s,S). Optimal stationary and periodic infinite-horizon policies. Applications to investment, options, overbooking, inventory, production, purchasing, selling, quality, repair, sequencing, queues, capacity, transportation. MATLAB is used. Prerequisites: probability, linear programming.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 252: Decision Analysis I: Foundations of Decision Analysis

Coherent approach to decision making, using the metaphor of developing a structured conversation having desirable properties, and producing actional thought that leads to clarity of action. Socratic instruction; computational problem sessions. Emphasis is on creation of distinctions, representation of uncertainty by probability, development of alternatives, specification of preference, and the role of these elements in creating a normative approach to decisions. Information gathering opportunities in terms of a value measure. Relevance and decision diagrams to represent inference and decision. Principles are applied to decisions in business, technology, law, and medicine. See 352 for continuation.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Howard, R. (PI)

MS&E 254: The Ethical Analyst

The ethical responsibility for consequences of professional analysts who use technical knowledge in support of any individual, organization, or government. The means to form ethical judgments; questioning the desirability of physical coercion and deception as a means to reach any end. Human action and relations in society in the light of previous thought, and research on the desired form of social interactions. Attitudes toward ethical dilemmas through an explicit personal code.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Howard, R. (PI)

MS&E 255: Decision Systems I

(Formerly MS&E 451.) Professional tools and techniques for designing decision systems that help when facing decisions such as buying a car, bidding on the Internet, hiring NFL players, making charitable donations, or choosing medical treatment. Demonstrations; small project. Topics: automatic decision diagram formulation, decision-class analysis, and dynamic sensitivity analysis. No programming required. Recommended: 252 or equivalent.
Terms: not given this year | Units: 2-3 | Grading: Letter or Credit/No Credit

MS&E 256: Technology Assessment and Regulation of Medical Devices

(Formerly 475.) Regulatory approval and reimbursement for new medical technologies as a key component of product commercialization. The regulatory and payer environment in the U.S. and abroad, and common methods of health technology assessment. Framework to identify factors relevant to adoption of new medical devices, and the management of those factors in the design and development phases. Case studies; guest speakers from government (FDA) and industry.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Pietzsch, J. (PI)

MS&E 260: Introduction to Operations Management

Operations management focuses on the effective planning, scheduling, and control of manufacturing and service entities. This course introduces students to a broad range of key issues in operations management. Topics include determination of optimal facility location, production planning, optimal timing and sizing of capacity expansion, and inventory control. Prerequisites: basic knowledge of Excel spreadsheets, probability, and optimization.
Terms: Aut, Sum | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Erhun Oguz, F. (PI)

MS&E 261: Inventory Control and Production Systems

Topics in the planning and control of manufacturing systems. The functions of inventory, determination of order quantities and safety stocks, alternative inventory replenishment systems, item forecasting, production-inventory systems, materials requirements planning (MRP), just-in-time systems, master and operations scheduling, supply chain management, and service operations. Limited enrollment. Prerequisite: 120, or STATS 116, or equivalent.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Hausman, W. (PI)

MS&E 262: Supply Chain Management

Definition of a supply chain; coordination difficulties; pitfalls and opportunities in supply chain management; inventory/service tradeoffs; performance measurement and incentives. Global supply chain management; mass customization; supplier management. Design and redesign of products and processes for supply chain management; tools for analysis; industrial applications; current industry initiatives. Enrollment limited to 50. Admission determined in the first class meeting. Prerequisite: 260 or 261.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Hausman, W. (PI)

MS&E 264: Sustainable Product Development and Manufacturing

Strategies and techniques for development of sustainable products and manufacturing processes. Topics: strategic decisions in new product development when environmental and resource externalities are accounted for; effect of regulatory requirements on ability of a firm to achieve its business objectives; contributions of sustainable products/processes to the firm's competitive advantage and operational efficiency and to enabling entrepreneurial opportunities; industrial ecology and life cycle analysis techniques in integrating traditional product development requirements with those of the environment and society. Maybe repeatable for credit once.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Rafinejad, D. (PI)

MS&E 266: Management of New Product Development

Techniques of managing or leading the process of new product development that have been found effective. Emphasis is placed on how much control is desirable and how that control can be exercised in a setting where creativity has traditionally played a larger role than discipline. Topics: design for manufacturability, assessing the market, imposing discipline on the new product development process, selecting the appropriate portfolio of new product development projects, disruptive technology, product development at internet speed, uncertainty in product development, role of experimentation in new product development, creating an effective development organization, and developing products to hit cost targets.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Rafinejad, D. (PI)

MS&E 268: Operations Strategy

The development and implementation of the operations functional strategy. The integration of operations strategy with business and corporate strategies of a manufacturing-based firm. Topics: types and characteristics of manufacturing technologies, quality management, capacity planning and facilities choice, organization and control of operations, and operations' role in corporate strategy. Prerequisites: 260 or 261, or equivalent experience.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kessinger, C. (PI)

MS&E 270: Strategy in Technology-Based Companies

For graduate students only. Introduction to the basic concepts of strategy, with emphasis on high technology firms. Topics: competitive positioning, resource-based perspectives, co-opetition and standards setting, and complexity/evolutionary perspectives. Limited enrollment.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Katila, R. (PI)

MS&E 271: Global Entrepreneurial Marketing

Skills needed to market new technology-based products to customers around the world. Case method discussions. Cases include startups and global high tech firms. Course themes: marketing toolkit, targeting markets and customers, product marketing and management, partners and distribution, sales and negotiation, and outbound marketing. Team-based take-home final exam. Limited enrollment. Admission by application.
Terms: Win, Spr | Units: 3-4 | Grading: Letter (ABCD/NP)

MS&E 272: Startup Boards

Accelerate your startup through hands-on guidance from a board of venture capitalists and experienced entrepreneurs custom built for your team. Like real startup boards, your board will help your team identify critical milestones, assist in achieving them, and hold your team accountable through regular board meetings. Learn how to avoid common mistakes which lead to ineffective board meetings, fired CEOs, and sometimes even the failure of an otherwise promising venture. Topics include designing a board, recruiting board members, managing board meetings, making strategic decisions, conflicts of interests, fiduciary responsibilities, ethical responsibilities, and CEO succession. Limited enrollment. Admission by application. Preference given to teams with demonstrated commitment to a viable startup business.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Korver, C. (PI)

MS&E 273: Technology Venture Formation

Open to graduate students interested in technology driven start-ups. Provides the experience of an early-stage entrepreneur seeking initial investment, including: team building, opportunity assessment, customer development, go-to-market strategy, and IP. Teaching team includes serial entrepreneurs and venture capitalists. Student teams validate the business model using R&D plans and financial projections, and define milestones for raising and using venture capital. Final exam is an investment pitch delivered to a panel of top tier VC partners. In addition to lectures, teams interact with mentors and teaching team weekly. Enrollment limited. Recommended: 270, 271, or equivalent.
Terms: Aut | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Fuchs, J. (PI); Lyons, M. (PI); MacLean, A. (PI)

MS&E 274: Dynamic Entrepreneurial Strategy

Primarily for graduate students. How entrepreneurial strategy focuses on creating structural change or responding to change induced externally. Grabber-holder dynamics as an analytical framework for developing entrepreneurial strategy to increase success in creating and shaping the diffusion of new technology or product innovation dynamics. Topics: First mover versus follower advantage in an emerging market; latecomer advantage and strategy in a mature market; strategy to break through stagnation; and strategy to turn danger into opportunity. Modeling, case studies, and term project.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tse, E. (PI)

MS&E 276: Entrepreneurial Management and Finance

For graduate students only with a preference for engineering and science majors. Emphasis on managing the challenges high-growth ventures experience, especially those based on technology products and services. Students develop a set of skills and approaches to becoming effective entrepreneurial managers. Topics include business model management, deal structure and negotiation, raising capital and financial management, venture operations and organizational administration, managing the interplay between ownership and growth, and handling adversity as well as failure. Limited enrollment. Admission by application. Prerequisite: 140/240, or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Byers, T. (PI); Loy, T. (PI)

MS&E 277: Creativity and Innovation

Experiential course explores factors that promote and inhibit creativity and innovation in individuals, teams, and organizations. Teaches creativity tools using workshops, case studies, field trips, expert guests, and team design challenges. Enrollment limited to 40. Admission by application. See http://creativity.stanford.edu.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Britos Cavagnaro, L. (PI); Seelig, T. (PI)

MS&E 278: Patent Law and Strategy for Innovators and Entrepreneurs (ME 208)

Inventors and entrepreneurs have four concerns related to patent law: protecting their inventions in the very early stages of product development, determining the patentability of their invention, avoiding infringement of a competitor's patent, and leveraging their patent as a business asset. This course will address each of these concerns through the application of law cases and business cases to an invention of the Studentâ¿¿s choice. Although listed as a ME/MSE course, the course is not specific to any discipline or technology.
Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Schox, J. (PI)

MS&E 280: Organizational Behavior: Evidence in Action

Organization theory; concepts and functions of management; behavior of the individual, work group, and organization. Emphasis is on cases and related discussion. Enrollment limited; priority to MS&E students.
Terms: Aut, Win | Units: 3-4 | Grading: Letter (ABCD/NP)

MS&E 283: Scaling up Excellence in Organizations

A problem for every manager is to make 'good' behaviors spread quickly and to shrink 'undesirable' behaviors quickly. This course provides you practical frameworks to accomplish these managerial goals. We will examine issues such as scaling Idea generation, scaling knowledge sharing, scaling the adoption of ideas across firms, scaling change in global firms. We will be using a newly written series of cases for this course and also draw on guest speakers.
Terms: Win | Units: 4 | Grading: Letter (ABCD/NP)

MS&E 289: Designing for Sustainable Abundance

Hands-on, team-based, multidisciplinary class, uses radically human-centered approach to tackle sustainability challenges in areas like food and transportation. Teams develop solutions that improve environmental and economic sustainability as well as physical and emotional well-being. Students benefit from close interaction with the teaching team, support from project sponsors, and the varied perspectives of numerous guest speakers. Application required. Limited enrollment. Design Institute class; see http://dschool.stanford.edu.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Dunn, D. (PI); Rothe, M. (PI)

MS&E 292: Health Policy Modeling

Primarily for master's students; also open to undergraduates and doctoral students. The application of mathematical, statistical, economic, and systems models to problems in health policy. Areas include: disease screening, prevention, and treatment; assessment of new technologies; bioterrorism response; and drug control policies.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Brandeau, M. (PI)

MS&E 293: Technology and National Security (MS&E 193, MS&E 193W)

The interaction of technology and national security policy from the perspective of history to implications for the new security imperative, homeland defense. Key technologies in nuclear and biological weapons, military platforms, and intelligence gathering. Policy issues from the point of view of U.S. and other nations. The impact of terrorist threat. Guest lecturers include key participants in the development of technology and/or policy. Students seeking to fulfill the WIM requirement should register for 193W.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 294: Climate Policy Analysis

Design and application of formal analytical methods in climate policy development. Issues include instrument design, technology development, resource management, multiparty negotiation, and dealing with complexity and uncertainty. Links among art, theory, and practice. Emphasis is on integrated use of modeling tools from diverse methodologies and requirements for policy making application. Recommended: background in economics, optimization, and decision analysis.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 295: Energy Policy Analysis

Design and application of formal analytical methods for policy and technology assessments of energy efficiency and renewable energy options. Emphasis is on integrated use of modeling tools from diverse methodologies and requirements for policy and corporate strategy development. Recommended: background in economics, optimization, and decision analysis.
Terms: Win, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Weyant, J. (PI)

MS&E 299: Voluntary Social Systems

Ethical theory, feasibility, and desirability of a social order in which coercion by individuals and government is minimized and people pursue ends on a voluntary basis. Topics: efficacy and ethics; use rights for property; contracts and torts; spontaneous order and free markets; crime and punishment based on restitution; guardian-ward theory for dealing with incompetents; the effects of state action-hypothesis of reverse results; applications to help the needy, armed intervention, victimless crimes, and environmental protection; transition strategies to a voluntary society.
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Howard, R. (PI)

MS&E 300: Ph.D. Qualifying Tutorial or Paper

Restricted to Ph.D. students assigned tutorials as part of the MS&E Ph.D. qualifying process. Enrollment optional.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

MS&E 301: Dissertation Research

Prerequisite: doctoral candidacy.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit

MS&E 310: Linear Programming

Formulation of standard linear programming models. Theory of polyhedral convex sets, linear inequalities, alternative theorems, and duality. Variants of the simplex method and the state of art interior-point algorithms. Sensitivity analyses, economic interpretations, and primal-dual methods. Relaxations of harder optimization problems and recent convex conic linear programs. Applications include game equilibrium facility location. Prerequisite: MATH 113 or consent of instructor.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ye, Y. (PI)

MS&E 311: Optimization

Applications, theories, and algorithms for finite-dimensional linear and nonlinear optimization problems with continuous variables. Elements of convex analysis, first- and second-order optimality conditions, sensitivity and duality. Algorithms for unconstrained optimization, and linearly and nonlinearly constrained problems. Modern applications in communication, game theory, auction, and economics. Prerequisites: MATH 113, 115, or equivalent.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 312: Advanced Methods in Numerical Optimization (CME 334)

Topics include interior-point methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. May be repeated for credit.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Murray, W. (PI)

MS&E 313: Vector Space Optimization

Optimization theory from the unified framework of vector space theory: treating together problems of mathematical programming, calculus of variations, optimal control, estimation, and other optimization problems. Emphasis is on geometric interpretation. Duality theory. Topics: vector spaces including function spaces; Hilbert space and the projection theorem; dual spaces and the separating hyperplane theorem; linear operators and adjoints; optimization of functionals, including theory of necessary conditions in general spaces, and convex optimization theory; constrained optimization including Fenchel duality theory. Prerequisite: MATH 115.
Terms: Aut, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Luenberger, D. (PI)

MS&E 314: Linear and Conic Optimization with Applications (CME 336)

Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Algorithms include the interior-point, barrier function, and cutting plane methods. Related convex analysis, including the separating hyperplane theorem, Farkas lemma, dual cones, optimality conditions, and conic inequalities. Complexity and/or computation efficiency analysis. Applications to combinatorial optimization, sensor network localization, support vector machine, and graph realization. Prerequisite: MS&E 211 or equivalent.
Terms: Win, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ye, Y. (PI)

MS&E 315: Numerical Optimization (CME 304)

Solution of nonlinear equations; unconstrained optimization; linear programming; quadratic programming; global optimization; general linearly and nonlinearly constrained optimization. Theory and algorithms to solve these problems. Prerequisite: background in analysis and numerical linear algebra.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Murray, W. (PI)

MS&E 316: Discrete Mathematics and Algorithms (CME 305)

Topics: enumeration such as Cayley's theorem and Prufer codes, SDR, flows and cuts (deterministic and randomized algorithms), probabilistic methods and random graphs, asymptotics (NP-hardness and approximation algorithms). Topics illustrated with EE, CS, and bioinformatics applications. Prerequisites: MATH 51 or 103 or equivalents.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 317: Algorithms for Modern Data Models (CS 263)

We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of CS 261.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 318: Large-Scale Numerical Optimization (CME 338)

The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basic factorization and updates. Interior methods. The reduced-gradient method, augmented Lagrangian methods, and SQP methods. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315; CME 108, 200, 302, 304, 334, or 335.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Saunders, M. (PI)

MS&E 319: Approximation Algorithms

Combinatorial and mathematical programming techniques to derive approximation algorithms for NP-hard optimization problems. Prossible topics include: greedy algorithms for vertex/set cover; rounding LP relaxations of integer programs; primal-dual algorithms; semidefinite relaxations. May be repeated for credit. Prerequisites: 112 or CS 161.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Saberi, A. (PI)

MS&E 321: Stochastic Systems

Topics in stochastic processes, emphasizing applications. Markov chains in discrete and continuous time; Markov processes in general state space; Lyapunov functions; regenerative process theory; renewal theory; martingales, Brownian motion, and diffusion processes. Application to queueing theory, storage theory, reliability, and finance. Prerequisites: 221 or STATS 217; MATH 113, 115. (Glynn)
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Glynn, P. (PI)

MS&E 322: Stochastic Calculus and Control

Ito integral, existence and uniqueness of solutions of stochastic differential equations (SDEs), diffusion approximations, numerical solutions of SDEs, controlled diffusions and the Hamilton-Jacobi-Bellman equation, and statistical inference of SDEs. Applications to finance and queueing theory. Prerequisites: 221 or STATS 217: MATH 113, 115.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 323: Stochastic Simulation

Emphasis is on the theoretical foundations of simulation methodology. Generation of uniform and non-uniform random variables. Discrete-event simulation and generalized semi-Markov processes. Output analysis (autoregressive, regenerative, spectral, and stationary times series methods). Variance reduction techniques (antithetic variables, common random numbers, control variables, discrete-time, conversion, importance sampling). Stochastic optimization (likelihood ratio method, perturbation analysis, stochastic approximation). Simulation in a parallel environment. Prerequisite: MS&E 221 or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 332: Security and Risk in Computer Networks

Risk management of large scale computing and networking systems with respect to security, data integrity, performance collapse, and service disruption. Qualitative and analytical basis for assessment, modeling, control, and mitigation of network risks. Stochastic risk models. Contact process. Random fields on networks. Virus and worm propagation dynamics and containment. Denial of service attacks. Intruder detection technologies. Distributed network attacks and countermeasures. Disaster recovery networks. Network protection services and resource placement. Autonomic self-defending networks. Economics of risk management. Emphasis is on analytics and quantitative methods.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 335: Queueing and Scheduling in Processing Networks

Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Stability analysis of queueing systems. Key results on single queues and queueing networks. Controlled queueing systems. Dynamic routing and scheduling in processing networks. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Prerequisite: 221 or equivalent.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bambos, N. (PI)

MS&E 336: Topics in Game Theory with Engineering Applications

Seminar. Recent research applying economic methods to engineering problems. Recent topics include: incentives in networked systems; mechanism design in engineered systems; and dynamics and learning in games. Prerequisites: mathematics at the level of MATH 115; game theory at the level of 246 or ECON 203; probability at the level of 220; optimization at the level of 211. May be repeated for credit.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 337: Information Networks (CME 337)

Network structure of the Internet and the web. Modeling, scale-free graphs, small-world phenomenon. Algorithmic implications in searching and inter-domain routing; the effect of structure on performance. Game theoretic issues, routing games, and network creation games. Security issues, vulnerability, and robustness. Prerequisite: basic probability and graph theory.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 338: Advanced Topics in Information Science and Technology

Advanced material in this area is sometimes taught for the first time as a topics course. Prerequisite: consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Van Roy, B. (PI)

MS&E 342: Advanced Investment Science

Topics: forwards and futures contracts, continuous and discrete time models of stock price behavior, geometric Brownian motion, Ito's lemma, basic options theory, Black-Scholes equation, advanced options techniques, models and applications of stochastic interest rate processes, and optimal portfolio growth. Computational issues and general theory. Teams work on independent projects. Prerequisite: 242.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Luenberger, D. (PI)

MS&E 347: Credit Risk: Modeling and Management

Credit risk modeling, valuation, and hedging emphasizing underlying economic, probabilistic, and statistical concepts. Point processes and their compensators. Structural, incomplete information and reduced form approaches. Single name products: corporate bonds, equity, equity options, credit and equity default swaps, forwards and swaptions. Multiname modeling: index and tranche swaps and options, collateralized debt obligations. Implementation, calibration and testing of models. Industry and market practice. Data and implementation driven group projects that focus on problems in the financial industry. Prerequisites: stochastic processes at the level of MSE 321, 322 or equivalent, and financial engineering at the level of MSE 342, MATH 180, MATH 240, FINANCE 622 or equivalent
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Giesecke, K. (PI)

MS&E 348: Optimization of Uncertainty and Applications in Finance

How to make optimal decisions in the presence of uncertainty, solution techniques for large-scale systems resulting from decision problems under uncertainty, and applications in finance. Decision trees, utility, two-stage and multi-stage decision problems, approaches to stochastic programming, model formulation; large-scale systems, Benders and Dantzig-Wolfe decomposition, Monte Carlo sampling and variance reduction techniques, risk management, portfolio optimization, asset-liability management, mortgage finance. Projects involving the practical application of optimization under uncertainty to financial planning.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 349: Capital Deployment

Methods for efficiently allocating capital among alternatives, constructing business plans, determining the value of risky projects, and creating alternatives that enhance value. Prerequisites: 242, 342.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 351: Dynamic Programming and Stochastic Control

Markov population decision chains in discrete and continuous time. Risk posture. Present value and Cesaro overtaking optimality. Optimal stopping. Successive approximation, policy improvement, and linear programming methods. Team decisions and stochastic programs; quadratic costs and certainty equivalents. Maximum principle. Controlled diffusions. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. MATLAB. Prerequisites: MATH 113, 115; Markov chains; linear programming.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Van Roy, B. (PI)

MS&E 352: Decision Analysis II: Professional Decision Analysis

How to organize the decision conversation, the role of the decision analysis cycle and the model sequence, assessing the quality of decisions, framing decisions, the decision hierarchy, strategy tables for alternative development, creating spare and effective decision diagrams, biases in assessment, knowledge maps, uncertainty about probability. Sensitivity analysis, approximations, value of revelation, joint information, options, flexibility, bidding, assessing and using corporate risk attitude, risk sharing and scaling, and decisions involving health and safety. See 353 for continuation. Prerequisite: 252.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Howard, R. (PI)

MS&E 353: Decision Analysis III: Frontiers of Decision Analysis

The concept of decision composite; probabilistic insurance and other challenges to the normative approach; the relationship of decision analysis to classical inference and data analysis procedures; the likelihood and exchangeability principles; inference, decision, and experimentation using conjugate distributions; developing a risk attitude based on general properties; alternative decision aiding practices such as analytic hierarchy and fuzzy approaches. Student presentations on current research. Goal is to prepare doctoral students for research. Prerequisite: 352.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Howard, R. (PI)

MS&E 355: Influence Diagrams and Probabilistics Networks

Network representations for reasoning under uncertainty: influence diagrams, belief networks, and Markov networks. Structuring and assessment of decision problems under uncertainty. Learning from evidence. Conditional independence and requisite information. Node reductions. Belief propagation and revision. Simulation. Linear-quadratic-Gaussian decision models and Kalman filters. Dynamic processes. Bayesian meta-analysis. Prerequisites: 220, 252, or equivalents, or consent of instructor.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 364: Multi-echelon Inventory Models

Theoretical treatment of control problems arising in inventory management, production, and distribution systems. Inventory control for single and multi-location systems. Emphasis is on operating characteristics, performance measures, and optimal operating and control policies. Dynamic programming and applications in inventory control. Prerequisite: STATS 217 or equivalent, linear programming.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 365: Advanced Models in Operations Management

Primarily for doctoral students. Focus on quantitative models dealing with sustainability and related to operations management. Prerequisite: consent of instructor. May be repeated for credit.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Hausman, W. (PI)

MS&E 371: Innovation and Strategic Change

Doctoral research seminar, limited to Ph.D. students. Current research on innovation strategy. Topics: scientific discovery, innovation search, organizational learning, evolutionary approaches, and incremental and radical change. Topics change yearly. Recommended: course in statistics or research methods.
Terms: alternate years, given next year | Units: 2-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 372: Entrepreneurship Doctoral Research Seminar

Classic and current research on entrepreneurship. Limited enrollment, restricted to PhD students. Prerequisites: SOC 363 or equivalent, and permission of instructor.
Terms: Win, alternate years, not given next year | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Eesley, C. (PI)

MS&E 374: Dynamic Corporate Strategy

Restricted to Ph.D. students. Research on the creation and shaping of disruptive industry dynamics and how companies can formulate and implement strategies to excel in such changing environments. Dynamic system model approach; case studies. Prerequisites: 201 or equivalent, 274.
Terms: Spr, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tse, E. (PI)

MS&E 375: Research on Entrepreneurship

Restricted to Ph.D. students. Organization theory, economics, and strategy perspectives. Limited enrollment. Prerequisites: SOC 360 or equivalent, and consent of instructor.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 376: Strategy Doctoral Research Seminar

Classic and current research on business and corporate strategy. Limited enrollment, restricted to PhD students. Prerequisites: SOC 363 or equivalent, and permission of instructor. Course may be repeated for credit.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Eisenhardt, K. (PI)

MS&E 380: Doctoral Research Seminar in Organizations

Limited to Ph.D. students. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Sutton, R. (PI)

MS&E 381: Doctoral Research Seminar in Work, Technology, and Organization

Enrollment limited to Ph.D. students. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.
Terms: alternate years, given next year | Units: 2-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 383: Doctoral Seminar on Ethnographic Research

For graduate students; upper-level undergraduates with consent of instructor. Ethnosemantic interviewing and participant observation. Techniques for taking, managing, and analyzing field notes and other qualitative data. 15 hours per week outside class collecting and analyzing own data. Methods texts and ethnographies offer examples of how to analyze and communicate ethnographic data. Prerequisite: consent of instructor. (Barley)
Terms: Win, alternate years, not given next year | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Barley, S. (PI)

MS&E 384: Groups and Teams

Research on groups and teams in organizations from the perspective of organizational behavior and social psychology. Topics include group effectiveness, norms, group composition, diversity, conflict, group dynamics, temporal issues in groups, geographically distributed teams, and intergroup relations.
Terms: Win, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hinds, P. (PI)

MS&E 389: Seminar on Organizational Theory (EDUC 375A, SOC 363A)

The social science literature on organizations assessed through consideration of the major theoretical traditions and lines of research predominant in the field.
Terms: Aut | Units: 5 | Grading: Letter (ABCD/NP)
Instructors: Powell, W. (PI)

MS&E 390: Doctoral Research Seminar in Health Systems Modeling

Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in health policy, health systems modeling, and health innovation. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Brandeau, M. (PI)

MS&E 391: Doctoral Research Seminar in Energy-Environmental Systems Modeling and Analysis

Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in energy and environmental modeling and analysis. Current emphasis on approaches to incorporation of uncertainty and technology dynamics into complex systems models. May be repeated for credit.
Terms: Aut, Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Weyant, J. (PI)

MS&E 408: Directed Reading and Research

Directed study and research on a subject of mutual interest to student and faculty member. Prerequisite: faculty sponsor. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 444: Investment Practice

Theory of real options, soft derivatives, and related ideas. Problems from financial engineering and risk management. Examples from industry. Small group projects formulate and design solutions to actual industry problems. Enrollment limited to 30. Admission by application.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)

MS&E 445: Projects in Wealth Management

Recent theory and standard practice in portfolio design for institutions, individuals, and funds. Student projects and case studies derived from the financial industry.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Woehrmann, P. (PI)

MS&E 446: Policy and Economics Research Roundtable (PERR)

Research in progress or contemplated in policy and economics areas. Emphasis depends on research interests of participants, but is likely to include energy, environment, transportation, or technology policy and analysis. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Sweeney, J. (PI)

MS&E 450: Lessons in Decision Making

Entrepreneurs, senior management consultants, and executives from Fortune 500 companies share real-world stories and insights from their experience in decision making.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Howard, R. (PI)

MS&E 452: Decision Analysis Projects: Helping Real Leaders Make Real Decisions

A virtual consulting firm directed by professional decision analysts who offer advice and guidance as student teams help local organizations make a current business strategy or public policy decision. Projects for businesses, governments, or other institutions typically include start-up venture funding, R&D portfolio planning, new product or market entry, acquisition or partnering, cost reduction, program design, or regulatory policy decisions. Emphasis is on developing clarity of action and delivering insights to clients. Satisfies MS&E project course requirement. Prerequiste: 252. Recommended: 352.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Robinson, B. (PI)

MS&E 453: Decision Analysis Applications: Business Strategy and Public Policy

How decision analysis is used to make decisions in organizations. Who applies these methods to what decisions, and when, where, and why. Case studies: entrepreneurial ventures, consulting projects, litigation, chip manufacturing, consumer electronics, Corvette design, blockbuster movies, R&D priorities, real estate portfolios, HIV/HCV drug trial design, cancer diagnostics, Mars contamination, oil E&P, economics and energy pricing, nuclear waste, climate change, marine resources, bioterrorism preparedness, nuclear weapons control, effective interactions, and ethics. Corequisite: MS&E 252 recommended.
Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Robinson, B. (PI)

MS&E 454: Decision Analysis Seminar

Current research and related topics presented by doctoral students and invited speakers. May be repeated for credit. Prerequisite: 252.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Howard, R. (PI)

MS&E 464: Global Project Coordination

Students engage in projects that are global in nature, and related to the planning, design, and operations of supply chains, marketing, manufacturing, and product development. Project teams from Stanford and an overseas university work on common projects using telephones, faxes, email, Internet, video conferences, and face-to-face meetings. As part of the project, students travel to Hong Kong. Applications due in November. See http://www.stanford.edu/class/msande464/.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Chiu, S. (PI); Cohen, S. (PI)

MS&E 472: Entrepreneurial Thought Leaders' Seminar

Entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. ETL speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks followed by half hour of class interaction. Required web discussion. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

MS&E 485: Cross-Cultural Design

International design research is in high demand, but is difficult, expensive and time consuming. Has technology finally developed enough to allow meaningful cross-cultural design and collaboration without getting on an airplane (or with limited travel)? Focus on using design ethnography to understand users in different national cultures (U.S. and Chile) and leveraging this understanding to inform the design of products. Project-based with teams composed of Stanford University and Universidad Católica (UC) students working concurrently at both locations around a real design opportunity. When exploring the cross-cultural collaboration space, we will address all three areas: technology, design management, and cultural understanding. Will involve travel for a limited subset of the students. Design Institute course: http://dschool.stanford.edu.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 491: Clean Energy Developement

Clean energy project class for graduate students committed to clean energy and entrepreneurship, strong analytic and communication skills, and serious individual and group work. Teams will conceive, prepare and present business plan for a clean energy project or company. Class sessions devoted to guidance necessary for team projects and outside guest speakers. Grades based on team performance in development and presentation of a business concept, outline and plan. Enrollment limited to 30. Admission by application.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

ME 10AX: Design Thinking and the Art of Innovation

This seminar will introduce students to techniques that designers use to create highly innovative solutions to wicked problems that cross domains. The project-based class will emphasize approaches to problem identification and problem solving. Along with a survey of tools such as need finding, structured brainstorming, synthesis, rapid prototyping, and visual communication, the class will include field trips to a local design firm, a robotics lab, and a prototyping lab. A secondary goal of the seminar is to introduce students to the pleasures of creative design and hands-on development of tangible solutions. Design has a unique approach to looking at both the problem domain and the solution domain in issues where technology, social issues, human behavior, and business needs overlap.
Terms: Sum | Units: 2 | Grading: Satisfactory/No Credit

ME 10N: Form and Function of Animal Skeletons (BIOE 10N)

Preference to freshmen. The biomechanics and mechanobiology of the musculoskeletal system in human beings and other vertebrates on the level of the whole organism, organ systems, tissues, and cell biology. Field trips to labs.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

ME 11SC: The Art and Science of Measuring Fluid Flows

The roles of fluid flows in natural systems such as swimming protozoa and planet-forming nebulae, and technologies such as biomolecular assay devices and jet engines. The analytical background for fluid sciences. Phenomena such as shock waves and vortex formation that create flow patterns while challenging engineers. Visualization and measurement techniques to obtain full-field flow pattern information. The physics behind these technologies. Field trips; lab work. (Eaton)
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

ME 12N: The Jet Engine

Preference to freshmen. How a jet engine works; the technologies and analytical techniques required to understand them. Dynamics, thermodynamics, turbomachinery, combustion, advanced materials, cooling technologies, and control systems. Visits to research laboratories, examination of a partially disassembled engine, and probable operation of a small jet engine. Prerequisites: high school physics.
Terms: Aut | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Eaton, J. (PI)

ME 12SC: Hands-on Jet Engines

How jet engines transformed the world through intercontinental travel causing internationalization in daily life. Competition driving improvements in fuel economy, engine lifetime, noise, and emissions.
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

ME 14N: How Stuff Is Made

The design and engineering of products and processes, such as machining, fabric, food, and electrical goods. Tradeoffs in choice of materials, features, and process selection. Final project: students research and redesign the engineering and manufacturing aspects of a product and its processes with an eye toward sustainability. Includes several field trips to manufacturing facilities.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Pruitt, B. (PI)

ME 15: Pre-field Course for Alternative Spring Break: Design for a Sustainable World

Preparation for Alternative Spring Break trip Design for a Sustainable World: Using the design method to create human-centered solutions to address the challenges of global poverty and sustainability. Limited to students participating in the Alternative Spring Break program. See http://asb.stanford.edu for more information.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit

ME 16: Pre-field Course for Alternative Spring Break: Design for a Sustainable World

Limited to students participating in the Alternative Spring Break program. See http://asb.stanford.edu for more information. Preparation for Alternative Spring Break trip Design for a Sustainable World: Using the design method to create human-centered solutions to address the challenges of global poverty and sustainability.
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 16N: The Science of Flames

Preference to freshmen. The roles that chemistry and fluid dynamics play in governing the behaviors of flames. Emphasis is on factors that affect flame microstructure, external appearance, and on the fundamental physical and chemical processes that cause flames and fires to propagate. Topics: history, thermodynamics, and pollutant formation in flames. Trips to labs where flames are studied. Prerequisites: high school physics.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

ME 17N: Robotics Imitating Nature

Preference to freshmen. The dream of constructing robots that duplicate the functional abilities of humans and/or other animals has been promulgated primarily by science fiction writers. But biological systems provide models for the designers of robots. Building electromechanical devices that perform locomotory and sensing functions similar to those of an animal as a way of learning about how biological systems function. Walking and running machines, and the problem of giving a robot the capability to respond to its environment.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 18Q: Teamology: Creative Teams and Individual Development

Preference to sophomores. Roles on a problem solving team that best suit individual creative characteristics. Two teams are formed for teaching experientially how to develop less conscious abilities from teammates creative in those roles. Reinforcement teams have members with similar personalities; problem solving teams are composed of people with maximally different personalities.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Wilde, D. (PI)

ME 19: Pre-field Course for Alternative Spring Break: Design for Social Change

Focus is on applying design, technology and innovation to catalyze social change. Topics include identifying social needs, learning different brainstorming methods, developing an applicable service model or product, prototyping, implementation, and reiteration. Reading and service components, followed by week-long Alternative Spring Break trip. See http://d4sc.blogspot.com. Enrollment limited to 12. May be repeated for credit.
Terms: not given this year | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 21N: Renaissance Machine Design

Preference to freshmen. Technological innovations of the 1400s that accompanied the proliferation of monumental art and architecture by Brunelleschi, da Vinci, and others who designed machines and invented novel construction, fresco, and bronze-casting techniques. The social and political climate, from the perspective of a machine designer, that made possible and demanded engineering expertise from prominent artists. Hands-on projectsto provide a physical understanding of Renaissance-era engineering challenges and introduce the pleasure of creative engineering design. Technical background not required.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

ME 23Q: The Worldly Engineer

Preference given to sophomores. Engineering, its practice and products placed in multi-disciplinary context. Topics include the history of the engineering profession and engineering education; cultural influences on design; the role of national and international public policy and economics; dependence on natural resources; environmental impact; contemporary workforce development. Emphasis is on cultivating an appreciation of these issues to enrich the educational and professional pursuit of engineering.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Su, L. (PI)

ME 25N: Energy Sustainability and Climate Change

One of the primary global challenges of the 21st century is providing the energy required to meet increasing demands due to population growth and economic development. A related challenge is mitigation of the effect of this energy growth on climate. This seminar will examine various scenarios for the energy resources required to meet future demand and the potential consequences on climate. The scientific issues underlying climate change and the coupling of energy use with changes in the global atmosphere that impact climate will be discussed.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bowman, C. (PI)

ME 26N: Think Like a Designer

Introduces students to techniques designers use to create highly innovative solutions across domains. The project-based class will emphasize approaches to problem identification and problem solving. Topics include need-finding, structured brainstorming, synthesis, rapid prototyping, and visual communication; field trips to a local design firm, a robotics lab, and a machining lab. A secondary goal of the seminar is to introduce students to the pleasures of creative design and hands-on development of tangible solutions.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Banerjee, S. (PI)

ME 27SI: Needfinding for Underserved Populations

The heart of any design process resides in empathy with users and their needs. Working in the realm of public service may engage a population to which the designer might not have been exposed. How different needfinding techniques can help designers to understand users from underserved populations and inspire them to create products and services that serve user needs.
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

ME 29D: Design for Diversity: Collaboration by difference in digital age

The focus of this course is on applying design, technology, and social innovation to create an environment that fosters collaboration by difference. Students will learn how in digital age their cultural, social and ethnic differences amplify and create unique opportunities for them to bring about social change. They will learn resocializing skills through somatic literacy to understand the otherâ¿¿s point of view. By the end of the quarter they will use design thinking tools to prototype a diversity imaginarium (Diversitarium), a portable structure and process, that amplifies and capitalizes different points of view to create an appreciation of voice and value to design socially meaningful product and processes.
Terms: Win | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Waldron, M. (PI)

ME 29SI: Cars: A Crash Course

Focus is on the basic mechanics and significance of cars. Topics include a basic, real-world understanding of automobile workings, histories, industries, cultural impact, and related media. Field trips to Tesla Motors and Go-Kart Racer will be organized, and there will be guest appearances by local automotive historians and enthusiasts. Students will get hands on experience with maintaining real cars, see high performance engines run, and have the opportunity to learn how to drive a manual transmission.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

ME 70: Introductory Fluids Engineering

Elements of fluid mechanics as applied to engineering problems. Equations of motion for incompressible ideal flow. Hydrostatics. Control volume laws for mass, momentum, and energy. Bernoulli equation. Dimensional analysis and similarity. Flow in ducts. Boundary layer flows. Lift and drag. Lab experiment demonstrations. Prerequisites: ENGR 14 and 30.
Terms: Win, Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

ME 80: Mechanics of Materials

Mechanics of materials and deformation of structural members. Topics include stress and deformation analysis under axial loading, torsion and bending, column buckling and pressure vessels. Introduction to stress transformation and multiaxial loading. Prerequisite: ENGR 14.
Terms: Aut, Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)

ME 101: Visual Thinking

Lecture/lab. Visual thinking and language skills are developed and exercised in the context of solving design problems. Exercises for the mind's eye. Rapid visualization and prototyping with emphasis on fluent and flexible idea production. The relationship between visual thinking and the creative process. Enrollment limited to 60.
Terms: Aut, Win, Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Benjamin, C. (PI); Kessin, J. (PI); Lopez, J. (PI); Northway, D. (PI); Phanichphant, P. (PI); Staff, 1. (PI); Whitsitt, L. (PI)

ME 103D: Engineering Drawing and Design

Designed to accompany 203. The fundamentals of engineering drawing including orthographic projection, dimensioning, sectioning, exploded and auxiliary views, assembly drawings, and SolidWorks. Homework drawings are of parts fabricated by the student in the lab. Assignments in 203 supported by material in 103D and sequenced on the assumption that the student is enrolled in both courses simultaneously.
Terms: Aut, Win, Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Kohn, M. (PI); Milroy, J. (PI)

ME 103N: Product Realization: Making is Thinking

Product Realization encompasses those processes required to transform a concept into the creation of a functional, useful, and beautiful product. In this project-based seminar, students develop product realization confidence and intuition using the rich array of tools available in the Product Realization Lab as well as industry-standard design engineering software programs and course readings in design/realization philosophy. Students develop a portfolio of products including soft goods, composite utensils, wearable electronics, mechatronics devices, and a final project of their own choosing. Interactions with the Stanford design engineering community as well as field trips to iconic Bay area design engineering firms round out students' experience. Learning Goals -Build confidence in transforming concepts into products through foundational texts and rigorous exercises -Master integrated design/realization software and tools through hands-on learning and practice -Engage with the Stanford design engineering community on campus and well beyond
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Beach, D. (PI); Edelman, J. (PI); Kohn, M. (PI)

ME 104: The Designer's Voice

Course helps students develop a point of view about their design career that will enable them to articulate their design vision, inspire a design studio, or infect a business with a culture of design-thinking. Focus on the integration of work and worldview, professional values, design language, and the development of the designer's voice. Includes seminar-style discussions, role-playing, short writing assignments, guest speakers, and individual mentoring and coaching. Participants will be required to keep a journal.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Burnett, W. (PI); Evans, D. (PI); Korsunskiy, Y. (PI); Williams, K. (PI)

ME 104B: Designing Your Life

The course employs a design thinking approach to help students develop a point of view about their career. The course focuses on an introduction to design thinking, the integration of work and worldview, and practices that support vocation formation. The course will include seminar-style discussions, role-playing, short writing assignments, guest speakers, and individual mentoring and coaching. Participants will be required to keep a journal. Enrollment limited to 54; Jrs. and Srs., all majors. Admission to be confirmed by email to Axess registered students prior to first class session. More information at www.designingyourlife.org. *As of Fall 2012, course is no longer repeatable for credit.
Terms: Aut, Win, Spr | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Burnett, W. (PI); Evans, D. (PI); Korsunskiy, Y. (PI); Williams, K. (PI)

ME 110: Design Sketching

Freehand sketching, rendering, and design development. Students develop a design sketching portfolio for review by program faculty. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Grossman, J. (PI); Li, W. (PI); Scott, W. (PI)

ME 112: Mechanical Systems Design

Lecture/lab. Characteristics of machine elements including gears, bearings, and shafts. Design for fatigue life. Electric motor fundamentals. Transmission design for maximizing output power or efficiency. Mechanism types, linkage analysis and kinematic synthesis. Team-based design projects emphasizing the balance of physical with virtual prototyping based on engineering analysis. Lab for dissection of mechanical systems and project design reviews. Prerequisites: 80, 101. Recommended: 203, ENGR 15.
Terms: Win | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Gerdes, J. (PI)

ME 113: Mechanical Engineering Design

Capstone course. Mechanical engineering design is experienced by students as they work on team projects obtained from industry or other organizations. Prerequisites: 80,101,112, 203. Enrollment priority to ME majors.
Terms: Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Staff, 1. (PI)

ME 115A: Introduction to Human Values in Design

Lecture/lab. Introduces the central philosophy of the product design program, emphasizing the relation between technical and human values, the innovation process, and design methodology. Lab exercises include development of simple product concepts visualized in rapidly executed three-dimensional mockups. Prerequisite: 101.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Kelley, D. (PI); Munro, J. (PI)

ME 115B: Product Design Methods

Problem-finding, problem-solving, intermediate creativity methods and effective techniques for researching and presenting product concepts. Individual- and team-based design projects emphasizing advanced visual thinking and prototyping skills. Prerequisite: ME115A
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Edson, J. (PI)

ME 115C: Design and Business Factors

Design and Business Factors: Introduces business concepts critical to determining the success of new products and services. Students will learn to estimate the cost of R&D for new product development. Using financial analysis, ROI, and tollgates to reduce development risk will be explored using case studies and simulations. Students will develop a bill of materials and a profit and loss statement for a sample product concept, prototype a design consultancy, and create a business proposal for a proposed new product company.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

ME 120: History and Philosophy of Design

Major schools of 19th- and 20th-century design (Arts and Crafts movement, Bauhaus, Industrial Design, and postmodernism) are analyzed in terms of their continuing cultural relevance. The relation of design to art, technology, and politics; readings from principal theorists, practitioners, and critics; recent controversies in industrial and graphic design, architecture, and urbanism. Enrollment limited to 65.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Katz, B. (PI)

ME 131A: Heat Transfer

The principles of heat transfer by conduction, convection, and radiation with examples from the engineering of practical devices and systems. Topics include transient and steady conduction, conduction by extended surfaces, boundary layer theory for forced and natural convection, boiling, heat exchangers, and graybody radiative exchange. Prerequisites: 70, ENGR 30. Recommended: intermediate calculus, ordinary differential equations.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Goodson, K. (PI)

ME 131B: Fluid Mechanics: Compressible Flow and Turbomachinery

Engineering applications involving compressible flow: aircraft and rocket propulsion, power generation; application of mass, momentum, energy and entropy balance to compressible flows; variable area isentropic flow, normal shock waves, adiabatic flow with friction, flow with heat addition. Operation of flow systems: the propulsion system. Turbomachinery: pumps, compressors, turbines. Angular momentum analysis of turbomachine performance, centrifugal and axial flow machines, effect of blade geometry, dimensionless performance of turbomachines; hydraulic turbines; steam turbines; wind turbines. Compressible flow turbomachinery: the aircraft engine. Prerequisites: 70, ENGR 30.
Terms: Win | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Eaton, J. (PI)

ME 139: Educating Young STEM Thinkers (EDUC 139X, EDUC 239X, ME 231)

The course will introduce students to the design thinking process, the national conversations about the future of STEM careers, and provide opportunities to work with middle school students and K-12 teachers in STEM-based after-school activities and intercession camps. The course will be both theory and practice focused. The purpose is twofold; to provide reflection and mentoring opportunities for students to learn about pathways to STEM careers and to introduce mentoring opportunities with young STEM thinkers.
Terms: Win, Spr | Units: 3-5 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Carroll, M. (PI); Goldman, S. (PI); Roth, B. (PI); Sheppard, S. (PI)

ME 140: Advanced Thermal Systems

Capstone course. Thermal analysis and engineering emphasizing integrating heat transfer, fluid mechanics, and thermodynamics into a unified approach to treating complex systems. Mixtures, humidity, chemical and phase equilibrium, and availability. Labs apply principles through hands-on experience with a turbojet engine, PEM fuel cell, and hybrid solid/oxygen rocket motor. Use of MATLAB as a computational tool. Prerequisites: ENGR 30, ME 70, and 131A,B.
Terms: Spr | Units: 5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Mitchell, R. (PI)

ME 161: Dynamic Systems, Vibrations and Control (ME 261)

(Graduate students only enroll in 261.) Modeling, analysis, and measurement of mechanical and electromechanical systems. Numerical and closed form solutions of ordinary differential equations governing the behavior of single and multiple degree of freedom systems. Stability, resonance, amplification and attenuation, and control system design. Prerequisite: background in dynamics and calculus such as ENGR 15 and MATH 43. Recommended: CME 102, and familiarity with differential equations, linear algebra, and basic electronics.
Terms: Aut | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Mitiguy, P. (PI)

ME 185: Electric Vehicle Design

This project based class focuses on the design and prototyping of electric vehicles. Students learn the fundamentals of vehicle design in class and apply the knowledge as they form teams and work on projects involving concept, specifications, structure, systems, integration, assembly, testing, etc. The class meets once a week to learn about the fundamentals, exchange their experiences, and coordinate between projects. The teams of 3-5 will work on their projects independently.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

ME 190: Ethical Issues in Mechanical Engineering

Moral rights and responsibilities of engineers in relation to society, employers, colleagues, and clients; cost-benefit-risk analysis, safety, and informed consent; whistle blowing; engineers as expert witnesses, consultants, and managers; ethical issues in engineering design, manufacturing, and operations, and engineering work in foreign countries; and ethical implications of the social and environmental contexts of contemporary engineering. Case studies and field research. Enrollment limited to 25 Mechanical Engineering majors.
Terms: not given this year | Units: 4 | Grading: Letter (ABCD/NP)

ME 191: Engineering Problems and Experimental Investigation

Directed study and research for undergraduates on a subject of mutual interest to student and staff member. Student must find faculty sponsor and have approval of adviser.
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 191H: Honors Research

Student must find faculty honors adviser and apply for admission to the honors program. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 196: Design and Manufacturing Forum (ME 396)

Invited speakers address issues of interest to design and manufacturing engineering and business students. Sponsored by the Product Realization Laboratory at Stanford.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Reis, R. (PI)

ME 201: Dim Sum of Mechanical Engineering

Introduction to research in mechanical engineering for M.S. students and upper-division undergraduates. Weekly presentations by current ME Ph.D. and second-year fellowship students to show research opportunities across the department. Strategies for getting involved in a research project. (Sheppard)
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 203: Design and Manufacturing

Integrated experience involving need finding, product definition, conceptual design, detail design, prototype manufacture, public presentation of outcomes, archiving and intrepreting the product realization process and its results. Presents an overview of manufacturing processes crucial to the practice of design. Corequisite: 103D or CAD experience. Corequisite for WIM for Mechanical Engineering and Product Design undergraduate majors: ENGR102M. Recommended: 101.
Terms: Aut, Win, Spr | Units: 4 | Grading: Letter (ABCD/NP)

ME 203X: Prototyping and Process Capture

Concepts and methods for low resolution prototyping as an integral activity in engineering design process. Class meetings include presentations by faculty and design oriented exercises by students. Assignments will be Blog Posts. ME203X is designed to work in phase with ME203 and offers greater depth in protoyping strategy, technique, and resultant insights. Concurrent enrollment in ME203 is required. Enrollment is optional and capped at 6 students.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

ME 204A: Bicycle Design and Frame-Building

Lecture/lab. The engineering and artistic execution of designing and building a bicycle frame. Fundamentals of bicycle dynamics, handling, and sizing. Manufacturing processes. Films, guest lecturers, field trips. Each student designs and fabricates a custom bicycle frame. This course is now a two part course series ME204A&B. Limited enrollment. Prerequisite: 203 or equivalent.
Terms: Win | Units: 1 | Grading: Letter (ABCD/NP)
Instructors: Connolly, R. (PI)

ME 204B: Bicycle Design and Frame-Building

The engineering and artistic execution of designing and building a bicycle frame. The fundamentals of bicycle dynamics, handling, and sizing. Manufacturing processes. Films, guest lecturers, field trips. Each student designs a custom bicycle frame that they continue from ME204A in winter quarter. Limited enrollment, admission by consent of instructors. Attendance at first lecture is required. Both ME204A and ME204B must be taken. Prerequisite: 203 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

ME 205: Flexible Part Design

Project based course. Students design and fabricate tooling to create and refine elastomeric parts using RTV silicone rubber. Focus is on the development of elastomeric part design intuition through iteration. Fabrication techniques include manual/CNC machining and additive manufacturing, and molding liquid silicone. Prerequisites: ME203 or instructor consent. Recommended: ME318. Admission is by consent of the instructor. Class size limited to 10, must attend first lecture.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kohn, M. (PI)

ME 206A: Entrepreneurial Design for Extreme Affordability

Project course jointly offered by School of Engineering and Graduate School of Business. Students apply engineering and business skills to design product prototypes, distribution systems, and business plans for entrepreneurial ventures in developing countries for a specified challenge faced by the world's poor. Topics include user empathy, appropriate technology design, rapid prototype engineering and testing, social technology entrepreneurship, business modeling, and project management. Weekly design reviews; final course presentation. Industry and adviser interaction. Limited enrollment via application; see http://www.stanford.edu/class/me206.
Terms: Win | Units: 4 | Grading: Letter (ABCD/NP)

ME 206B: Entrepreneurial Design for Extreme Affordability

Part two of two-quarter project course jointly offered by School of Engineering and Graduate School of Business. Second quarter emphasizes prototyping and implementation of specific projects identified in first quarter. Students work in cross-disciplinary project teams. Industry and adviser interaction, weekly design reviews; final course presentation. Prerequisite: 206A. (Jointly offered as GSB OIT333B) Design Institute class; see http://dschool.stanford.edu.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Patell, J. (PI)

ME 208: Patent Law and Strategy for Innovators and Entrepreneurs (MS&E 278)

Inventors and entrepreneurs have four concerns related to patent law: protecting their inventions in the very early stages of product development, determining the patentability of their invention, avoiding infringement of a competitor's patent, and leveraging their patent as a business asset. This course will address each of these concerns through the application of law cases and business cases to an invention of the Studentâ¿¿s choice. Although listed as a ME/MSE course, the course is not specific to any discipline or technology.
Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: Schox, J. (PI)

ME 210: Introduction to Mechatronics

Technologies involved in mechatronics (intelligent electro-mechanical systems), and techniques to apply this technology to mecatronic system design. Topics include: electronics (A/D, D/A converters, op-amps, filters, power devices); software program design, event-driven programming; hardware and DC stepper motors, solenoids, and robust sensing. Large, open-ended team project. Limited enrollment. Prerequisites: ENGR 40, CS 106, or equivalents.
Terms: Win | Units: 4 | Grading: Letter or Credit/No Credit

ME 211: ReMake ¿ Design Lessons from Restoration

Preference is given to early graduate and advanced undergraduate students. Course will focus on the restoration of the 1962 Cadillac DeVille project car as a design investigation. Topics include: what makes a car a classic? How does this car express luxury, and how is that different from contemporary luxury products? What does the car say about the American identity, and how has that changed over the past half-century? Every student can expect to get their hands dirty; prior automotive experience is not required but everyone is expected to be motivated to learn. Our goal is to have the car operational again by the end of the Fall Quarter. Enrollment limited to 15.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Steinert, R. (PI)

ME 212: Calibrating the Instrument

For first-year graduate students in the Joint Program in Design. Means for calibrating the designer's mind/body instrument through tools including improvisation, brainstorming, creative imaging, educational kinesiology, and Brain Gym. Current design issues; guest speakers; shared stories; and goal setting.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Edmark, J. (PI)

ME 214: Good Products, Bad Products (ME 314)

The characteristics of industrial products that cause them to be successes or failures: the straightforward (performance, economy, reliability), the complicated (human and cultural fit, compatibility with the environment, craftsmanship, positive emotional response of the user), the esoteric (elegance, sophistication, symbolism). Engineers and business people must better understand these factors to produce more successful products. Projects, papers, guest speakers, field trips.
Terms: Win | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Beach, D. (PI)

ME 216A: Advanced Product Design: Needfinding

Human needs that lead to the conceptualization of future products, environments, systems, and services. Field work in public and private settings; appraisal of personal values; readings on social ethnographic issues; and needfinding for a corporate client. Emphasis is on developing the flexible thinking skills that enable the designer to navigate the future. Prerequisites for undergraduates: 116 and 203, or consent of instructor.
Terms: Aut | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Patnaik, D. (PI)

ME 216B: Advanced Product Design: Implementation 1

Summary project using knowledge, methodology, and skills obtained in Product Design major. Students implement an original design concept and present it to a professional jury. Prerequisite: 216A.
Terms: Win | Units: 4 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Burnett, W. (PI); Staff, 1. (PI)

ME 216C: Advanced Product Design: Implementation 2

ME216C: Implementation II is a continuation of ME216B. Students would develop project from ME216B to a further state of completion. Design will be completed, details about manufacturing, cost and production will be developed. Students will validate their projects by making them real in the world. Prerequisites for class are ME216A and ME216B.Prerequisite: 216A and 216B.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)

ME 218A: Smart Product Design Fundamentals

Lecture/Lab. Team design project series on programmable electromechanical systems design. Topics: transistors as switches, basic digital and analog circuits, operational amplifiers, comparators, software design, state machines, programming in C. Lab fee. Limited enrollment.
Terms: Aut | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Carryer, J. (PI)

ME 218B: Smart Product Design Applications

Lecture/lab. Second in team design project series on programmable electromechanical systems design. Topics: user I/O, timer systems, interrupts, signal conditioning, software design for embedded systems, statecharts, sensors, actuators, noise, and power supplies. Lab fee. Limited enrollment. Prerequisite: 218A or passing the smart product design fundamentals proficiency examination.
Terms: Win | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Carryer, J. (PI)

ME 218C: Smart Product Design Practice

Lecture/lab. Advanced level in series on programmable electromechanical systems design. Topics: inter-processor communication, system design with multiple microprocessors, architecture and assembly language programming for the PIC microcontroller, controlling the embedded software tool chain, A/D and D/A techniques, electronic manufacturing technology. Team project. Lab fee. Limited enrollment. Prerequisite: 218B.
Terms: Spr | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Carryer, J. (PI)

ME 218D: Smart Product Design: Projects

Lecture/lab. Industrially sponsored project is the culmination of the Smart Product Design sequence. Student teams take on an industrial project requiring application and extension of knowledge gained in the prior three quarters, including prototyping of a final solution with hardware, software, and professional documentation and presentation. Lectures extend the students' knowledge of electronic and software design, and electronic manufacturing techniques. Topics: chip level design of microprocessor systems, real time operating systems, alternate microprocessor architectures, and PCB layout and fabrication. Prerequisite: 218C.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Carryer, J. (PI)

ME 219: The Magic of Materials and Manufacturing

ME219 is intended for design-oriented students who anticipate imagining and then creating new products with a focus on materiality and brand or design and business. ME219 assumes a basic knowledge of materials and manufacturing processes which results from taking E50, ME203, or equivalent course/life experience. Our graduates will acquire professional foundation information about materials and materiality from a product design point-of-view, manufacturing processes and business systems inside a factory, and story-telling by book authorship, essay writing, and multimedia presentation. We hope our graduates will exhibit a deep and life-long love of materials and manufacturing in order to make great products and tell a good story about each one.
Terms: Aut, Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Beach, D. (PI); Johnson, K. (PI); Switky, A. (PI)

ME 220: Introduction to Sensors

Sensors are widely used in scientific research and as an integral part of commercial products and automated systems. The basic principles for sensing displacement, force, pressure, acceleration, temperature, optical radiation, nuclear radiation, and other physical parameters. Performance, cost, and operating requirements of available sensors. Elementary electronic circuits which are typically used with sensors. Lecture demonstration of a representative sensor from each category elucidates operating principles and typical performance. Lab experiments with off-the-shelf devices.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Kenny, T. (PI)

ME 223: Innovating Water Solutions for Developing Countries

The future of global water resources is a challenge of immense salience. Currently 1.1 billion people lack safe drinking water and 2.6 billion people lack adequate sanitation. The United Nations¿ Food and Agriculture Organization states that by 2025, 1.9 billion people will be living in countries or regions with absolute water scarcity, and two-thirds of the world population could be under stress conditions. The Stanford ChangeLabs has initiated a project called the 100 Liter Water project, designed to form strategies that will deliver a minimum of 100 liters of water per day per family to the poorest communities in the world. This class is meant primarily for Graduate students and Seniors with strong design and mechanical engineering backgrounds. This is a self-directed project class is restricted to 15 students, who are selected through an application process. Please go to https://xxxx to apply for this project based class being offered in Fall 2012-13. Students are expected to work individually and in teams on specific water related technologies such as Solar based low flow pumping systems, rainwater catchment systems, and storage systems. The studio class will entail working on the design of Solar Powered low flow pumps, rainwater catchment systems, and very low cost storage systems designed for sparsely distributed communities in water stressed regions of the world. Students will be expected to work with autonomy and self-direction, going through multiple rounds of prototyping to generate breakthrough technologies designed to make deep impact.
Terms: Aut | Units: 2-3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Banerjee, S. (PI)

ME 227: Vehicle Dynamics and Control

The application of dynamics, kinematics, and control theory to the analysis and design of ground vehicle behavior. Simplified models of ride, handling, and braking, their role in developing intuition, and limitations in engineering design. Suspension design fundamentals. Performance and safety enhancement through automatic control systems. In-car laboratory assignments for model validation and kinesthetic understanding of dynamics. Limited enrollment. Prerequisites: ENGR 105, consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Gerdes, J. (PI)

ME 231: Educating Young STEM Thinkers (EDUC 139X, EDUC 239X, ME 139)

The course will introduce students to the design thinking process, the national conversations about the future of STEM careers, and provide opportunities to work with middle school students and K-12 teachers in STEM-based after-school activities and intercession camps. The course will be both theory and practice focused. The purpose is twofold; to provide reflection and mentoring opportunities for students to learn about pathways to STEM careers and to introduce mentoring opportunities with young STEM thinkers.
Terms: Win, Spr | Units: 3-5 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Carroll, M. (PI); Goldman, S. (PI); Roth, B. (PI); Sheppard, S. (PI)

ME 233: Making it Big: Crossing the Entrepreneur's Gap

Students learn to take novel designs into entrepreneurial production and prepare for market production. Education, resources, and community are provided to help students cross the gap, founding ideas and making them real, in volume. Topics include entrepreneurial production methods and initiation, vendor selection and engagement, cost, design transfer, quality and testing, manufacturing planning and execution. Course prepares students for leadership roles in entrepreneurial as well as large production-oriented companies. Case studies, regular project reviews, final presentation, industry interaction.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Theeuwes, M. (PI)

ME 236: Tales to Design Cars By

Investigating a personâ's relationship with cars through the application of research and with a generative storytelling focus will provide inspiration for designing a new automotive experience. This course will use ethnographic research, interviews, and a variety of narrative methods including verbal, non-verbal, cinema, and sound, and short collaborative projects to inform the creation of a physical prototype for a new car experience and the story around it. Restricted to co-term and graduate students.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Karanian, B. (PI)

ME 238: Patent Prosecution

(Same as LAW231) Stages of the patent application process: identifying, capturing, and evaluating inventions; performing a patentability investigation, analyzing the documents, and the scope of the patent protection; composing claims that broadly cover the invention; creating a specification that supports the claims; filing a patent application with the U.S. Patent and Trademark Office; and analyzing an office action and preparing an appropriate response. Current rules and case law. Strategic decisions within each stage, such as: how does a patent application advance the patent portfolio; and in what countries should a patent application be filed? Same as Law 321.
Terms: Win | Units: 2 | Grading: Letter (ABCD/NP)
Instructors: Schox, J. (PI)

ME 239: Mechanics of the Cell

Kinematical description of basic structural elements used to model parts of the cell: rods, ropes, membranes, and shells. Formulation of constitutive equations: nonlinear elasticity and entropic contributions. Elasticity of polymeric networks. Applications to model basic filaments of the cytoskeleton: actin, microtubules, intermediate filaments, and complete networks. Applications to biological membranes.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 250: Internal Combustion Engines

Internal combustion engines including conventional and turbocharged spark ignition, and diesel engines. Lectures: basic engine cycles, engine components, methods of analysis of engine performance, pollutant emissions, and methods of engine testing. Lab involves hands-on experience with engines and test hardware. Limited enrollment. Prerequisites: 140.
Terms: Aut | Units: 1-5 | Grading: Letter or Credit/No Credit
Instructors: Edwards, C. (PI)

ME 257: Turbine and Internal Combustion Engines (ME 357)

Principles of design analysis for aircraft gas turbines and automotive piston engines. Analysis for aircraft engines performed for Airbus A380 type aircraft. Design parameters determined considering aircraft aerodynamics, gas turbine thermodynamics, compressible flow physics, and material limitations. Additional topics include characteristics of main engine components, off-design analysis, and component matching. Performance of automotive piston engines including novel engine concepts in terms of engine thermodynamics, intake and exhaust flows, and in-cylinder flow.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

ME 260: Fuel Cell Science and Technology

Emphasis on proton exchange membrane (PEM) and solid oxide fuel cells (SOFC), and principles of electrochemical energy conversion. Topics in materials science, thermodynamics, and fluid mechanics. Prerequisites: MATH 43, PHYSICS 55, and ENGR 30 or ME 140, or equivalents.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Staff, 1. (PI)

ME 261: Dynamic Systems, Vibrations and Control (ME 161)

(Graduate students only enroll in 261.) Modeling, analysis, and measurement of mechanical and electromechanical systems. Numerical and closed form solutions of ordinary differential equations governing the behavior of single and multiple degree of freedom systems. Stability, resonance, amplification and attenuation, and control system design. Prerequisite: background in dynamics and calculus such as ENGR 15 and MATH 43. Recommended: CME 102, and familiarity with differential equations, linear algebra, and basic electronics.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Mitiguy, P. (PI)

ME 265: Technology Licencing and Commercialization

How to profit from technology; processes and strategies to commercialize functional or artistic inventions and creations (not limited to mechanical engineering). Business and legal aspects of determining what can be owned and licensed, how to determine commercial value, and what agreements are necessary. Contract and intellectual property law; focus is on provisions of license agreements and their negotiation.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hustein, J. (PI)

ME 266: Introduction to Physiology and Biomechanics of Hearing

Hearing is fundamental to our ability to communicate, yet in the US alone over 30 million people suffer some form of hearing impairment. As engineers and scientists, it is important for us to understand the underlying principles of the auditory system if we are to devise better ways of helping those with hearing loss. The goal of this course is to introduce undergraduate and graduate students to the anatomy, physiology, and biomechanics of hearing. Principles from acoustics, mechanics, and hydrodynamics will be used to build a foundational understanding of one of the most complex, interdisciplinary, and fascinating areas of biology. Topics include the evolution of hearing, computational modeling approaches, fluid-structure interactions, ion-channel transduction, psychoacoustics, diagnostic tools, and micrometer to millimeter scale imaging methods. We will also study current technologies for mitigating hearing loss via passive and active prostheses, as well as future regenerative therapies.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Puria, S. (PI)

ME 277: Graduate Design Research Techniques

ME 277 Design What? Graduate Design Research Techniques ¿ This class brings together students from different backgrounds to work on real-world design challenges. ¿ We will explore Design Thinking process with a particular emphasis on: ethnographic techniques, needfinding, framing and concept generation. ¿ We use the Design Thinking process as a lens to explore ways to better understand people and their culture. ¿ We use cultural differences as a source of design inspiration, with the understanding that design itself is a ¿culturally embedded practice
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)

ME 280: Skeletal Development and Evolution (BIOE 280)

The mechanobiology of skeletal growth, adaptation, regeneration, and aging is considered from developmental and evolutionary perspectives. Emphasis is on the interactions between mechanical and chemical factors in the regulation of connective tissue biology. Prerequisites: BIO 42, and ME 80 or BIOE 42.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 281: Biomechanics of Movement (BIOE 281)

Experimental techniques to study human and animal movement including motion capture systems, EMG, force plates, medical imaging, and animation. The mechanical properties of muscle and tendon, and quantitative analysis of musculoskeletal geometry. Projects and demonstrations emphasize applications of mechanics in sports, orthopedics, and rehabilitation.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Delp, S. (PI)

ME 283: Introduction to Biomechanics

Introduction to the application of mechanical engineering analysis to understand human physiology and disease. Topics include basics of musculoskeletal force analysis, cell mechanics, blood flow, and mechanical behaviors of tissues. Undergraduates should have taken ME 70 and ME 80 or equivalents.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Levenston, M. (PI)

ME 287: Mechanics of Biological Tissues

Introduction to the mechanical behaviors of biological tissues in health and disease. Overview of experimental approaches to evaluating tissue properties and mathematical constitutive models. Elastic behaviors of hard tissues, nonlinear elastic and viscoelastic models for soft tissues.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Levenston, M. (PI)

ME 288: ReDesigning Theater

In this class students will learn and apply the design thinking processes to reinvent the theater experience. Students will learn and then identify, define, needfind, ideate and prototype the elements necessary to create a new artistic genre of live performance that will utilize technology in new ways and embody what is unique to the Silicon Valley / San Francisco Bay Area. This multidisciplinary class will leverage different technical and creative disciplines to create an accessible and radical collaborative performance atmosphere.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Burnett, W. (PI); Sturtz, M. (PI)

ME 294: Medical Device Design

In collaboration with the School of Medicine. Introduction to medical device design for undergraduate and graduate engineering students. ME294 is the lecture portion of the class. For involvement with design and projects, co-enroll in the lab portion, ME294L.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit

ME 294L: Medical Device Design Lab

In collaboration with the School of Medicine. This is the lab portion of ME294, which must be taken concurrently. Introduction to medical device design for undergraduate and graduate engineering students. Design, prototyping and labs. Medical device environments may include hands-on device testing; and field trips to operating rooms and local device companies. Prerequisite: 203.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)

ME 297: Forecasting for Innovators:Technology, Tools & Social Change

Technologies from the steam engine to the microprocessor have been mixed gifts, at once benefitting humankind and creating many of the problems facing humanity today. This class will explore how innovators can use forecasting methods to identify new challenges, develop responsive innovations and anticipate unintended consequences. Students will produce a long-range forecast project, applying a variety of methodologies including research, expert interviews and graphical exploration.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Saffo, P. (PI)

ME 298: Silversmithing and Design

Skills involved in working with precious metals at a small scale. Investment casting and fabrication techniques such as reticulation, granulations, filigree, and mokume gane.
Terms: Win, Spr | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

ME 299A: Practical Training

For master's students. Educational opportunities in high technology research and development labs in industry. Students engage in internship work and integrate that work into their academic program. Following internship work, students complete a research report outlining work activity, problems investigated, key results, and follow-up projects they expect to perform. Meets the requirements for curricular practical training for students on F-1 visas. Student is responsible for arranging own internship/employment and faculty sponsorship. Register under faculty sponsor's section number. All paperwork must be completed by student and faculty sponsor, as the Student Services Office does not sponsor CPT. Students are allowed only two quarters of CPT per degree program. Course may be repeated twice.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Adams, J. (PI); Andriacchi, T. (PI); Banerjee, S. (PI); Barnett, D. (PI); Bazant, M. (PI); Beach, D. (PI); Bowman, C. (PI); Bradshaw, P. (PI); Burnett, W. (PI); Cai, W. (PI); Cantwell, B. (PI); Cappelli, M. (PI); Carryer, J. (PI); Carter, D. (PI); Chang, F. (PI); Cho, K. (PI); Cutkosky, M. (PI); Darve, E. (PI); Dauskardt, R. (PI); DeBra, D. (PI); Delp, S. (PI); Durbin, P. (PI); Eaton, J. (PI); Edwards, C. (PI); Enge, P. (PI); Farhat, C. (PI); Gao, H. (PI); Gerdes, J. (PI); Goodson, K. (PI); Hanson, R. (PI); Harris, J. (PI); Homsy, G. (PI); Hughes, T. (PI); Iaccarino, G. (PI); Ishii, K. (PI); Jacobs, C. (PI); Jameson, A. (PI); Johnston, J. (PI); Kasevich, M. (PI); Kelley, D. (PI); Kelly, M. (PI); Kembel, G. (PI); Kenny, T. (PI); Khatib, O. (PI); Kovacs, G. (PI); Kruger, C. (PI); Kuhl, E. (PI); Latombe, J. (PI); Leifer, L. (PI); Lele, S. (PI); Lentink, D. (PI); Levenston, M. (PI); Lew, A. (PI); Mani, A. (PI); Milroy, J. (PI); Mitchell, R. (PI); Mitiguy, P. (PI); Moin, P. (PI); Monismith, S. (PI); Mungal, M. (PI); Nelson, D. (PI); Niemeyer, G. (PI); Okamura, A. (PI); Pianetta, P. (PI); Pinsky, P. (PI); Pitsch, H. (PI); Powell, J. (PI); Prinz, F. (PI); Pruitt, B. (PI); Rock, S. (PI); Roth, B. (PI); Salisbury, J. (PI); Santiago, J. (PI); Shaqfeh, E. (PI); Sheppard, S. (PI); Sherby, O. (PI); Springer, G. (PI); Steele, C. (PI); Street, R. (PI); Stuart, A. (PI); Tang, S. (PI); Taylor, C. (PI); Toye, G. (PI); Tsai, S. (PI); Waldron, K. (PI); Zajac, F. (PI); Zheng, X. (PI)

ME 299B: Practical Training

For Ph.D. students. Educational opportunities in high technology research and development labs in industry. Students engage in internship work and integrate that work into their academic program. Following internship work, students complete a research report outlining work activity, problems investigated, key results, and follow-up projects they expect to perform. Meets the requirements for curricular practical training for students on F-1 visas. Student is responsible for arranging own internship/employment and faculty sponsorship. Register under faculty sponsor's section number. All paperwork must be completed by student and faculty sponsor, as the student services office does not sponsor CPT. Students are allowed only two quarters of CPT per degree program. Course may be repeated twice.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 300A: Linear Algebra with Application to Engineering Computations (CME 200)

Computer based solution of systems of algebraic equations obtained from engineering problems and eigen-system analysis, Gaussian elimination, effect of round-off error, operation counts, banded matrices arising from discretization of differential equations, ill-conditioned matrices, matrix theory, least square solution of unsolvable systems, solution of non-linear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, Cayley-Hamilton theory and function of a matrix and iterative methods. Prerequisite: familiarity with computer programming, and MATH104, 113, or equivalent.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Moin, P. (PI)

ME 300B: Partial Differential Equations in Engineering (CME 204)

Geometric interpretation of partial differential equation (PDE) characteristics; solution of first order PDEs and classification of second-order PDEs; self-similarity; separation of variables as applied to parabolic, hyperbolic, and elliptic PDEs; special functions; eigenfunction expansions; the method of characteristics. If time permits, Fourier integrals and transforms, Laplace transforms. Prerequisite: CME 200/ME 300A, equivalent, or consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lele, S. (PI)

ME 300C: Introduction to Numerical Methods for Engineering (AA 214A, CME 206)

Numerical methods from a user's point of view. Lagrange interpolation, splines. Integration: trapezoid, Romberg, Gauss, adaptive quadrature; numerical solution of ordinary differential equations: explicit and implicit methods, multistep methods, Runge-Kutta and predictor-corrector methods, boundary value problems, eigenvalue problems; systems of differential equations, stiffness. Emphasis is on analysis of numerical methods for accuracy, stability, and convergence. Introduction to numerical solutions of partial differential equations; Von Neumann stability analysis; alternating direction implicit methods and nonlinear equations. Prerequisites: CME 200/ME 300A, CME 204/ME 300B.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Iaccarino, G. (PI)

ME 301: LaunchPad:Design and Launch your Product or Service

Apply principles of design thinking to the real-life challenge of imagining, prototyping, testing and iterating, building, marketing, and selling your product or service. Work will be in teams (you apply as an intact team) or alone. You must submit a proposal and team for approval. Proposal can be a physical good or service of any kind. Projects are treated as real start-ups, so the work will be intense. Proposal submitted by Feb 15, 2010 acceptance by March 1. Design Institute class; see http://dschool.stanford.edu.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)

ME 302: The Future of the Automobile

The objective of this course is to develop an understanding for the requirements that go into the design of a highly complex yet easy-to-use product, i.e. the automobile. Students will learn about very different interdisciplinary aspects that characterize the automobile and personal mobility. In the first half of the quarter, the class will discuss different design parameters for everyone's favorite car and thereby realize certain characteristics and conflicts. In the second half of the quarter, guest speakers from academia and industry will share their vision regarding the future of the automobile and how design challenges are addressed within their respective organizations. At the end of the quarter, students will have developed a broader understanding of the intertwined technology - environmental - human - business - legal aspects that will shape the future of the automobile.
Terms: Aut, Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 303: Biomechanics of Flight

Study of biological flight as an inspiration for designing robots. The goal is to give students a broad understanding of the biomechanics of animal and plant flight, and a more in-depth understanding of bird flight. This course will elucidate how students can pick and choose exciting biological questions, use biological and engineering techniques to answer them, and use the results as an inspiration to develop innovative flying robots. Techniques discussed will range from theoretical approaches, programming, engineering measurement techniques, ethical aspects of biological research, reward based training techniques for animal flight experiments, to bio-inspired design thinking. Prerequisites: (fluid mechanics OR aerodynamics) AND (Matlab OR Arduino OR similar programming skills). This multidisciplinary course provides all interested ME, AA, and BIOE students a unique opportunity to learn how to integrate biology, engineering, and design.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

ME 304: The Designer's Voice

Course helps students develop a point of view about their design career that will enable them to articulate their design vision, inspire a design studio, or infect a business with a culture of design-thinking. Focus on the integration of work and worldview, professional values, design language, and the development of the designer¿s voice. Includes seminar-style discussions, role-playing, short writing assignments, guest speakers, and individual mentoring and coaching. Participants will be required to keep a journal.
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Burnett, W. (PI); Evans, D. (PI); Williams, K. (PI)

ME 307: Green's Function Methods in Engineering (MATSCI 307)

The concept of Green's Functions used to recast ordinary and partial dislocations as integral equations with built-in boundary conditions will be developed, including the inclusion of modified Green's Functions, where appropriate. Applications to the solutions of ODE's and elliptic, hyperbolic, and parabolic partial differential equations will be studied, including Laplace's equation, the wave and reduced wave equation, the diffusion/heat conduction equation, and the equations of motion of linear elastic theory. The course will be self-contained, so that a working knowledge of simple ODE's and the separation of variables method is the only prerequisite. Class notes will be provided.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Barnett, D. (PI)

ME 308: Spatial Motion

The geometry of motion in Euclidean space. Fundamentals of theory of screws with applications to robotic mechanisms, constraint analysis, and vehicle dynamics. Methods for representing the positions of spatial systems of rigid bodies with their inter-relationships; the formulation of Newton-Euler kinetics applied to serial chain systems such as industrial robotics.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

ME 309: Finite Element Analysis in Mechanical Design

Basic concepts of finite elements, with applications to problems confronted by mechanical designers. Linear static, modal, and thermal formulations emphasized; nonlinear and dynamic formulations introduced.Application of a commercial finite element code in analyzing design problems. Issues: solution methods, modeling techniques, features of various commercial codes, basic problem definition. Individual projects focus on the interplay of analysis and testing in product design/development. Prerequisites: Math 103, or equivalent. Recommended: ME80 or CEE101A, or equivalent in structural and/or solid mechanics; some exposure to principles of heat transfer.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Sheppard, S. (PI)

ME 310A: Project-Based Engineering Design, Innovation, and Development

Three quarter sequence; for engineering graduate students intending to lead projects related to sustainability, automotive, biomedical devices, communication, and user interaction. Student teams collaborate with academic partners in Europe, Asia, and Latin America on product innovation challenges presented by global corporations to design requirements and construct functional prototypes for consumer testing and technical evaluation. Design loft format such as found in Silicon Valley consultancies. Typically requires international travel. Prerequisites: undergraduate engineering design project; consent of instructor.
Terms: Aut | Units: 4 | Grading: Letter (ABCD/NP)

ME 310B: Project-Based Engineering Design, Innovation, and Development

Three quarter sequence; for engineering graduate students intending to lead projects related to sustainability, automotive, biomedical devices, communication, and user interaction. Student teams collaborate with academic partners in Europe, Asia, and Latin America on product innovation challenges presented by global corporations to design requirements and construct functional prototypes for consumer testing and technical evaluation. Design loft format such as found in Silicon Valley consultancies. Typically requires international travel. Prerequisites: undergraduate engineering design project; consent of instructor.
Terms: Win | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Leifer, L. (PI)

ME 310C: Project-Based Engineering Design, Innovation, and Development

Three quarter sequence; for engineering graduate students intending to lead projects related to sustainability, automotive, biomedical devices, communication, and user interaction. Student teams collaborate with academic partners in Europe, Asia, and Latin America on product innovation challenges presented by global corporations to design requirements and construct functional prototypes for consumer testing and technical evaluation. Design loft format such as found in Silicon Valley consultancies. Typically requires international travel. Prerequisites: undergraduate engineering design project; consent of instructor.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Leifer, L. (PI)

ME 310X: New Product Management

Restricted to graduate students. Focus is on the role of the product manager in industry. Topics include product management skills, leadership and team management, getting a product management job, corporate and project finance for engineers, sales and marketing for engineers and business strategy. Seminar with in-class exercises and guest speakers from industry. Limited to 50. Prerequisite: Enrolled ME310 students only.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Schar, M. (PI)

ME 312: Advanced Product Design: Formgiving

Lecture/lab. Small- and medium-scale design projects carried to a high degree of aesthetic refinement. Emphasis is on form development, design process, and model making. Prerequisites: 203, 313. Corequisite: ARTSTUDI 260.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Burnett, W. (PI)

ME 313: Human Values and Innovation in Design

Introduction to the philosophy, spirit, and tradition of the product design program. Hands-on design projects used as vehicles for design thinking, visualization, and methodology. The relationships among technical, human, aesthetic, and business concerns. Drawing, prototyping, and design skills. Focus is on tenets of design philosophy: point of view, user-centered design, design methodology, and iterative design.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)

ME 314: Good Products, Bad Products (ME 214)

The characteristics of industrial products that cause them to be successes or failures: the straightforward (performance, economy, reliability), the complicated (human and cultural fit, compatibility with the environment, craftsmanship, positive emotional response of the user), the esoteric (elegance, sophistication, symbolism). Engineers and business people must better understand these factors to produce more successful products. Projects, papers, guest speakers, field trips.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Beach, D. (PI)

ME 315: The Designer in Society

For graduate students. Career objectives and psychological orientation compared with existing social values and conditions. Emphasis is on assisting individuals in assessing their roles in society. Readings on political, social, and humanistic thought are related to technology and design. Experiential, in-class exercises, and term project. Enrollment limited to 24.
Terms: Spr | Units: 3 | Grading: Satisfactory/No Credit

ME 316A: Product Design Master's Project

For graduate Product Design or Design (Art) majors only. Student teams, under the supervision of the design faculty, spend the quarter researching master's project topics. Students are expected to demonstrate mastery of design thinking methods including; needfinding, brainstorming, field interviews and synthesis during this investigation. Masters projects are selected that involve the synthesis of aesthetics and technological concerns in the service of human need. Design Institute class; see http://dschool.stanford.edu. Prereq: ME216A, ME313, ME 311
Terms: Aut | Units: 2-6 | Grading: Letter (ABCD/NP)

ME 316B: Product Design Master's Project

Design Garage is a Winter/Spring class (a two quarter commitment is required). The class is a deep dive in design thinking that uses student-lead projects to teach design process and methods. The projects come from investigations conducted during the Fall quarter where the preliminary need finding, customer research, and product or service ideas have been developed to provide the ¿seed¿ projects for the student design teams. Students will learn the methodologies of design thinking by bringing a product, service, or experience to market. Students apply to Design Garage in the Fall, and teams are formed after interviews and applications are reviewed. Prerequisite: graduate student standing.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)

ME 316C: Product Design Master's Project

This is the second half of the two quarter Design Garage sequence. Students will complete projects begun in ME316B the prior quarter. Prerequisite: ME316C and graduate student standing. Design Institute class; see http://dschool.stanford.edu.
Terms: Spr | Units: 3-4 | Repeatable for credit | Grading: Letter (ABCD/NP)

ME 317A: Design Methods: Product Definition

Systematic methodologies to define, develop, and produce world-class products. Student team projects to identify opportunities for improvement and develop a comprehensive product definition. Topics include value engineering, quality function deployment, design for assembly and producibility, design for variety and supply chain, design for life-cycle quality, and concurrent engineering. Students must take 317B to complete the project and obtain a letter grade. On-campus enrollment limited to 20; SCPD class size limited to 50, and each site must have at least 3 students to form a project team.
Terms: Win | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Beiter, K. (PI)

ME 317B: Design Methods: Quality By Design

Building on 317A, focus is on the implementation of competitive product design. Student groups apply structured methods to optimize the design of an improved product, and plan for its manufacture, testing, and service. The project deliverable is a comprehensive product and process specification. Topics: concept generation and selection (Pugh's Method), FMEA applied to the manufacturing process, design for robustness, Taguchi Method, SPC and six sigma process, tolerance analysis, flexible manufacturing, product testing, rapid prototyping. Enrollment limited to 40, not including SCPD students. Minimum enrollment of two per SCPD viewing site; single student site by prior consent of instructor. On-campus class limited to 20. For SCPD students, limit is 50 and each site must have a minimum of three students to form a project team and define a project on their own. Prerequisite: 317A.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Beiter, K. (PI)

ME 318: Computer-Aided Product Creation

Design course focusing on an integrated suite of computer tools: rapid prototyping, solid modeling, computer-aided machining, and computer numerical control manufacturing. Students choose, design, and manufacture individual products, emphasizing individual design process and computer design tools. Field trips demonstrate Stanford Product Realization Lab's relationship to the outside world. Structured lab experiences build a basic CAD/CAM/CNC proficiency. Limited enrollment. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Milroy, J. (PI)

ME 319: Fundamentals of Design for Design Thinkers

This course is an introduction to the fundamental principles of Design, geared toward graduate students involved and invested in innovation and design thinking. Core concepts include Contrast, Color, Materiality, Form, Proportion, Transitions, and more. Students will be introduced to the major philosophical concepts of design in readings and in class, and will practice techniques in class and via weekly hands-on projects out of class, culminating in a final personal project. Students will also be introduced to many hands-on prototyping and making skills via access to the Product Realization Lab and Room 36 (webshop.stanford.edu)
Terms: Aut | Units: 2-4 | Grading: Satisfactory/No Credit

ME 320: Introduction to Robotics (CS 223A)

Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khatib, O. (PI)

ME 321: Optofluidics: Interplay of Light and Fluids at the Micro and Nanoscale

Many optical systems in biology have sophisticated designs with functions that conventional optics cannot achieve: no synthetic materials, for example, can provide the camouflage capability exhibited by some animals. This course overviews recent efforts--some inspired by examples in biology--in using fluids, soft materials and nanostructures to create new functions in optics. Topics include electrowetting lenses, electronic inks, colloidal photonic crystals, bioinspired optical nanostructures, nanophotonic biosensors, lens-less optofluidic microscopes. The use of optics to control fluids is also discussed: optoelectronic tweezers, particle trapping and transport, microrheology, optofluidic sorters, fabrication and self-assembly of novel micro and nanostructures.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tang, S. (PI)

ME 323: Modeling and Identification of Mechanical Systems for Control

Lecture/Lab. The art and science behind developing mathematical models for control system design. Theoretical and practical system modeling and parameter identification. Frequency domain identification, parametric modeling, and black-box identification. Analytical work and laboratory experience with identification, controller implementation, and the implications of unmodeled dynamics and non-linearities. Prerequisites: linear algebra and system simulation with MATLAB/SIMULINK; ENGR 105.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 324: Precision Engineering

Advances in engineering are often enabled by more accurate control of manufacturing and measuring tolerances. Concepts and technology enable precision such that the ratio of overall dimensions to uncertainty of measurement is large relative to normal engineering practice. Typical application areas: non-spherical optics, computer information storage devices, and manufacturing metrology systems. Application experience through design and manufacture of a precision engineering project, emphasizing the principles of precision engineering. Structured labs; field trips. Prerequisite: consent of instructors.
Terms: Spr | Units: 4 | Grading: Letter or Credit/No Credit

ME 327: Design and Control of Haptic Systems

Study of the design and control of haptic systems, which provide touch feedback to human users interacting with virtual environments and teleoperated robots. Focus is on device modeling (kinematics and dynamics), synthesis and analysis of control systems, design and implementation, and human interaction with haptic systems. Coursework includes homework/laboratory assignments and a research-oriented project. Directed toward graduate students and advanced undergraduates in engineering and computer science. Prerequisites: dynamic systems and MATLAB programming. Suggested experience with C/C++ programming and feedback control design.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Okamura, A. (PI)

ME 328: Medical Robotics

Study of the design and control of robots for medical applications. Focus is on robotics in surgery and interventional radiology, with introduction to other healthcare robots. Delivery is through instructor lectures and weekly guest speakers. Coursework includes homework and laboratory assignments, an exam, and a research-oriented project. Directed toward graduate students and advanced undergraduates in engineering and computer science; no medical background required. Prerequisites: dynamic systems and MATLAB programming. Suggested experience with C/C++ programming, feedback control design, and linear systems. Cannot be taken concurrently with CS 571.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Barbagli, F. (PI); Okamura, A. (PI)

ME 331A: Advanced Dynamics & Computation

Newton, Euler, momentum, and road-map methods and computational tools for 3-D force and motion analysis of multibody systems. Power, work, and energy. Numerical solutions (e.g., MATLAB, etc.) of nonlinear algebraic and differential equations governing the static and dynamic behavior of multiple degree of freedom systems.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Mitiguy, P. (PI)

ME 331B: Advanced Dynamics, Simulation & Control

Advanced methods and computational tools for the efficient formulation of equations of motion for multibody systems. D'Alembert principle. Power, work, and energy. Kane's method. Lagrange's method. Computed torque control. Systems with constraints. Quaternions Numerical solutions of nonlinear algebraic and differential equations governing the behavior of multiple degree of freedom systems.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Mitiguy, P. (PI)

ME 333: Mechanics

Goal is a common basis for advanced mechanics courses. Introduction to variation calculus. Formulation of the governing equations from a Lagrangian perspective for finite and infinite dimensional mechanical systems. Examples include systems of particles and linear elastic solids. Introduction to tensors. Definition and interpretation of Cauchy stress tenor.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lew, A. (PI)

ME 335A: Finite Element Analysis

Fundamental concepts and techniques of primal finite element methods. Method of weighted residuals, Galerkin's method and variational equations. Linear eliptic boundary value problems in one, two and three space dimensions; applications in structural, solid and fluid mechanics and heat transfer. Properties of standard element families and numerically integrated elements. Implementation of the finite element method using Matlab, assembly of equations, and element routines. Lagrange multiplier and penalty methods for treatment of constraints. The mathematical theory of finite elements.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pinsky, P. (PI)

ME 335B: Finite Element Analysis

Finite element methods for linear dynamic analysis. Eigenvalue, parabolic, and hyperbolic problems. Mathematical properties of semi-discrete (t-continuous) Galerkin approximations. Modal decomposition and direct spectral truncation techniques. Stability, consistency, convergence, and accuracy of ordinary differential equation solvers. Asymptotic stability, over-shoot, and conservation laws for discrete algorithms. Mass reduction. Applications in heat conduction, structural vibrations, and elastic wave propagation. Computer implementation of finite element methods in linear dynamics. Implicit, explicit, and implicit-explicit algorithms and code architectures.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pinsky, P. (PI)

ME 335C: Finite Element Analysis

Variational formulation of nonlinear elliptic, parabolic and hyperbolic problems. Newton's method for solving nonlinear algebraic systems; load-stepping, convergence, divergence and bifurcation. Enhancement of Newton's method including line-search, quasi-Newton and arc-length methods. Finite element approximation and consistent linearization; definition of the tangent operator and residual vector. Data structures for nonlinear finite element analysis. Examples drawn from nonlinear (rigid) heat conduction, nonlinear elasticity and contact mechanics.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 336: Crystalline Anisotropy (MATSCI 359)

Matrix and tensor analysis with applications to the effects of crystal symmetry on elastic deformation, thermal expansion, diffusion, piezoelectricity, magnetism, thermodynamics, and optical properties of solids, on the level of J. F. Nye's Physical Properties of Crystals. Homework sets use Mathematica.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Barnett, D. (PI)

ME 337: Mechanics of Growth

Introduction to continuum theory and computational simulation of living matter. Kinematics of finite growth. Balance equations in open system thermodynamics. Constitutive equations for living systems. Custom-designed finite element solution strategies. Analytical solutions for simple model problems. Numerical solutions for clinically relevant problems such as: bone remodeling; wound healing; tumor growth; atherosclerosis; heart failure; tissue expansion; and high performance training.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kuhl, E. (PI)

ME 338: Continuum Mechanics

Linear and nonlinear continuum mechanics for solids. Introduction to tensor algebra and tensor analysis. Kinematics of motion. Balance equations of mass, linear and angular momentum, energy, and entropy. Constitutive equations of isotropic and anisotropic hyperelasticity. Recommended as prerequisite for Finite Element Methods.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kuhl, E. (PI)

ME 338B: Continuum Mechanics

Constitutive theory; equilibrium constitutive relations; material frame indifference and material symmetry; finite elasticity; formulation of the boundary value problem; linearization and well-posedness; symmetries and configurational forces; numerical considerations.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

ME 339: Introduction to parallel computing using MPI, openMP, and CUDA (CME 213)

This class will give hands on experience with programming multicore processors, graphics processing units (GPU), and parallel computers. Focus will be on the message passing interface (MPI, parallel clusters) and the compute unified device architecture (CUDA, GPU). Topics will include: network topologies, modeling communication times, collective communication operations, parallel efficiency, MPI, dense linear algebra using MPI. Symmetric multiprocessing (SMP), pthreads, openMP. CUDA, combining MPI and CUDA, dense linear algebra using CUDA, sort, reduce and scan using CUDA. Pre-requisites include: C programming language and numerical algorithms (solution of differential equations, linear algebra, Fourier transforms).
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Darve, E. (PI)

ME 340: Theory and Applications of Elasticity

This course provides an introduction to the elasticity theory and its application to material structures at microscale. The basic theory includes the definition of stress, strain and elastic energy; equilibrium and compatibility conditions; and the formulation of boundary value problems. We will mainly discuss the stress function method to solve 2D problems and will briefly discuss the Green's function approach for 3D problems. The theory and solution methods are then applied to contact problems as well as microscopic defects in solids, such as voids, inclusions, cracks, and dislocations. Computer programming in Matlab is used to aid analytic derivation and numerical solutions of elasticity problems.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cai, W. (PI)

ME 342: Theory and Application of Inelasticity

Theories of plasticity and fracture phenomena from both phenomenological and micromechanical viewpoints. Yield surface, flow rules, strain hardening models, and applications to creep. Plastic zone near crack tip. Linear fracture mechanics and other criteria for crack initiation and growth. Application to fatigue. Classical analytic solutions will be discussed together with numerical solutions of plane elastoplatic problems by Matlab.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cai, W. (PI)

ME 342A: MEMS Laboratory

Practice and theory of MEMS device design and fabrication, orientation to fabrication facilities, and introduction to techniques for design and evaluation of MEMS devices in the context of designed projects. Emphasis on MEMS design (need finding, brainstorming, evaluation, and design methodology), characterization, and fabrication, including photolithography, etching, oxidation, diffusion, and ion implanation. Limited enrollment. Prerequisite: engineering or science background and consent of instructor.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

ME 342D: MEMS Laboratory Assignments

Prerequisite: consent of instructor.
Terms: Sum | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Pruitt, B. (PI)

ME 345: Fatigue Design and Analysis

The mechanism and occurrences of fatigue in service. Methods for predicting fatigue life and for protecting against premature fatigue failure. Use of elastic stress and inelastic strain analyses to predict crack initiation life. Use of linear elastic fracture mechanics to predict crack propagation life. Effects of stress concentrations, manufacturing processes, load sequence, irregular loading, multi-axial loading. Subject is treated from the viewpoints of the engineer seeking up-to-date methods of life prediction and the researcher interested in improving understanding of fatigue behavior. Prerequisite: undergraduate mechanics of materials.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 346A: Introduction to Statistical Mechanics

The main purpose of this course is to provide students with enough statistical mechanics background to the Molecular Simulations classes (ME 346B,C), including the fundamental concepts such as ensemble, entropy, and free energy, etc. The main theme of this course is how the laws at the macroscale (thermodynamics) can be obtained by analyzing the spontaneous fluctuations at the microscale (dynamics of molecules). Topics include thermodynamics, probability theory, information entropy, statistical ensembles, phase transition and phase equilibrium. Recommended: PHYSICS 110 or equivalent.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

ME 346B: Introduction to Molecular Simulations

Algorithms of molecular simulations and underlying theories. Molecular dynamics, time integrators, modeling thermodynamic ensembles (NPT, NVT), free energy, constraints. Monte Carlo simulations, parallel tempering. Stochastic equations, Langevin and Brownian dynamics. Applications in solids, liquids, and biomolecules (proteins). Programming in Matlab.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

ME 346C: Advanced Techniques for Molecular Simulations

Advanced methods for computer simulations of solids and molecules. Methods for long-range force calculation, including Ewald methods and fast multipole method. Methods for free energy calculation, such as thermodynamic integration. Methods for predicting rates of rare events (e.g. nucleation), including nudged elastic band method and umbrella sampling method. Students will work on projects in teams.
Terms: Sum | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Cai, W. (PI)

ME 347: Mathematical Theory of Dislocations

The mathematical theory of straight and curvilinear dislocations in linear elastic solids. Stress fields, energies, and Peach-Koehler forces associated with these line imperfections. Anisotropic effects, Green's function methods, and the geometrical techniques of Brown and Indenborn-Orlov for computing dislocation fields and for studying dislocation interactions. Continuously distributed dislocations and cracks and inclusions.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 348: Experimental Stress Analysis

Theory and applications of photoelasticity, strain gages, and holographic interferometry. Comparison of test results with theoretical predictions of stress and strain. Discussion of other methods of stress and strain determination (optical fiber strain sensors, acoustoelasticity, thermoelasticity, brittle coating, Moire interferometry, residual stress determination). Six labs plus mini-project. Limited enrollment. Lab fee.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 349: Variational Methods in Elasticity and Plate Theory

An introduction to variational calculus methods and their applications to the theories of elasticity and plates.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 351A: Fluid Mechanics

Exact and approximate analysis of fluid flow covering kinematics, global and differential equations of mass, momentum, and energy conservation. Forces and stresses in fluids. Euler¿s equations and the Bernoulli theorem applied to inviscid flows. Vorticity dynamics. Topics in irrotational flow: stream function and velocity potential for exact and approximate solutions; superposition of solutions; complex potential function; circulation and lift. Some boundary layer concepts.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Su, L. (PI)

ME 351B: Fluid Mechanics

Laminar viscous fluid flow. Governing equations, boundary conditions, and constitutive laws. Exact solutions for parallel flows. Creeping flow limit, lubrication theory, and boundary layer theory including free-shear layers and approximate methods of solution; boundary layer separation. Introduction to stability theory and transition to turbulence, and turbulent boundary layers. Prerequisite: 351A.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Mani, A. (PI)

ME 352A: Radiative Heat Transfer

The fundamentals of thermal radiation heat transfer; blackbody radiation laws; radiative properties of non-black surfaces; analysis of radiative exchange between surfaces and in enclosures; combined radiation, conduction, and convection; radiative transfer in absorbing, emitting, and scattering media. Advanced material for students with interests in heat transfer, as applied in high-temperature energy conversion systems. Take 352B,C for depth in heat transfer. Prerequisites: graduate standing and undergraduate course in heat transfer. Recommended: computer skills.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Mitchell, R. (PI)

ME 352B: Fundamentals of Heat Conduction

Physical description of heat conduction in solids, liquids, and gases. The heat diffusion equation and its solution using analytical and numerical techniques. Data and microscopic models for the thermal conductivity of solids, liquids, and gases, and for the thermal resistance at solid-solid and solid-liquid boundaries. Introduction to the kinetic theory of heat transport, focusing on applications for composite materials, semiconductor devices, micromachined sensors and actuators, and rarefied gases. Prerequisite: consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Goodson, K. (PI)

ME 352C: Convective Heat Transfer

Prediction of heat and mass transfer rates based on analytical and numerical solutions of the governing partial differential equations. Heat transfer in fully developed pipe and channel flow, pipe entrance flow, laminar boundary layers, and turbulent boundary layers. Superposition methods for handling non-uniform wall boundary conditions. Approximate models for turbulent flows. Comparison of exact and approximate analyses to modern experimental results. General introduction to heat transfer in complex flows. Prerequisite: 351B or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Eaton, J. (PI)

ME 354: Experimental Methods in Fluid Mechanics

Experimental methods associated with the interfacing of laboratory instruments, experimental control, sampling strategies, data analysis, and introductory image processing. Instrumentation including point-wise anemometers and particle image tracking systems. Lab. Prerequisites: previous experience with computer programming and consent of instructor. Limited enrollment.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Santiago, J. (PI)

ME 355: Compressible Flow

Topics include quasi-one-dimensional isentropic flow in variable area ducts, normal shock waves, oblique shock and expansion waves, flow in ducts with friction and heat transfer, unsteady one-dimensional flow, and steady two-dimensional supersonic flow.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 357: Turbine and Internal Combustion Engines (ME 257)

Principles of design analysis for aircraft gas turbines and automotive piston engines. Analysis for aircraft engines performed for Airbus A380 type aircraft. Design parameters determined considering aircraft aerodynamics, gas turbine thermodynamics, compressible flow physics, and material limitations. Additional topics include characteristics of main engine components, off-design analysis, and component matching. Performance of automotive piston engines including novel engine concepts in terms of engine thermodynamics, intake and exhaust flows, and in-cylinder flow.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

ME 358: Heat Transfer in Microdevices

Application-driven introduction to the thermal design of electronic circuits, sensors, and actuators that have dimensions comparable to or smaller than one micrometer. The impact of thin-layer boundaries on thermal conduction and radiation. Convection in microchannels and microscopic heat pipes. Thermal property measurements for microdevices. Emphasis is on Si and GaAs semiconductor devices and layers of unusual, technically-promising materials such as chemical-vapor-deposited (CVD) diamond. Final project based on student research interests. Prerequisite: consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Asheghi, M. (PI)

ME 359A: Advanced Design and Engineering of Space Systems I

The application of advanced theory and concepts to the development of spacecraft and missile subsystems; taught by experts in their fields. Practical aspects of design and integration. Mission analysis, systems design and verification, radiation and space environments, orbital mechanics, space propulsion, electrical power and avionics subsystems, payload communications, and attitude control. Subsystem-oriented design problems focused around a mission to be completed in groups. Tours of Lockheed Martin facilities. Limited enrollment. Prerequisites: undergraduate degree in related engineering field or consent of instructor.
Terms: not given this year | Units: 4 | Grading: Letter or Credit/No Credit

ME 359B: Advanced Design and Engineering of Space Systems II

Continuation of 359A. Topics include aerospace materials, mechanical environments, structural analysis and design, finite element analysis, mechanisms, thermal control, probability and statistics. Tours of Lockheed Martin facilities. Limited enrollment. Prerequisites: undergraduate degree in related field, or consent of instructor.
Terms: not given this year | Units: 4 | Grading: Letter or Credit/No Credit

ME 361: Turbulence

The nature of turbulent flows, statistical and spectral description of turbulence, coherent structures, spatial and temporal scales of turbulent flows. Averaging, two-point correlations and governing equations. Reynolds averaged equations and stresses. Free shear flows, turbulent jet, turbulent kinetic energy and kinetic energy dissipation, and kinetic energy budget. Kolmogorov's hypothesis and energy spectrum. Wall bounded flows, viscous scales, and law of the wall. Turbulence closure modeling for Reynolds averaged Navier Stokes equations. Direct and large eddy simulation of turbulent flows. Subgrid scale modeling.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Mani, A. (PI)

ME 362A: Physical Gas Dynamics

Concepts and techniques for description of high-temperature and chemically reacting gases from a molecular point of view. Introductory kinetic theory, chemical thermodynamics, and statistical mechanics as applied to properties of gases and gas mixtures. Transport and thermodynamic properties, law of mass action, and equilibrium chemical composition. Maxwellian and Boltzmann distributions of velocity and molecular energy. Examples and applications from areas of current interest such as combustion and materials processing.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cappelli, M. (PI)

ME 362B: Nonequilibrium Processes in High-Temperature Gases

Chemical kinetics and energy transfer in high-temperature gases. Collision theory, transition state theory, and unimolecular reaction theory. Prerequisie: 362A or consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hanson, R. (PI)

ME 366: Creative Gym: A Design Thinking Skills Studio

Build your creative confidence and sharpen your design thinking skills. Train your intuition and expand the design context from which you operate every day. This experimental studio will introduce the d.school to fast-paced experimental exercises that lay the mental and physical foundation for a potent bias toward action, and a deeper knowledge of the personal skills that expert design thinkers utilize in all phases of their process. Exercises will be offered by a number of the d.school's most creatively confident design thinkers. Apply at the first day of class.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit

ME 367: Optical Diagnostics and Spectroscopy Laboratory

Principles, procedures, and instrumentation associated with optical measurements in gases and plasmas. Absorption, fluorescence and emission, and light-scattering methods. Measurements of temperature, species concentration, and molecular properties. Lab. Enrollment limited to 16. Prerequisite: 362A or 364.
Terms: Spr | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Hanson, R. (PI)

ME 368: d.Leadership: Design Leadership in Context

d.Leadership is a course that teaches the coaching and leadership skills needed to drive good design process in groups. Preference given to students who have taken other Design Group or d.school classes. d.leaders will work on real projects driving design projects within organizations outside campus, and gain real world skills to lead innovation in groups. Take this course if you are inspired by past design classes and want skills to lead design projects beyond Stanford.
Terms: Win | Units: 1-3 | Grading: Letter (ABCD/NP)
Instructors: Klebahn, P. (PI); Utley, J. (PI)

ME 368A: Biodesign Innovation: Needs Finding and Concept Creation (BIOE 374A, MED 272A)

(Same as OIT 384) Two quarter sequence. Inventing new medical devices and instrumentation, including: methods of validating medical needs; techniques for analyzing intellectual property; basics of regulatory (FDA) and reimbursement planning; brainstorming and early prototyping. Guest lecturers and practical demonstrations.
Terms: Win | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)

ME 368B: Biodesign Innovation: Concept Development and Implementation (BIOE 374B, MED 272B)

(Same as OIT 385) Two quarter sequence. How to take a medical device invention forward from early concept to technology translation and development. Topics include prototyping; patent strategies; advanced planning for reimbursement and FDA approval; choosing translation route (licensing versus start-up); ethical issues including conflict of interest; fundraising approaches and cash requirements; essentials of writing a business or research plan; strategies for assembling a development team. Prerequisite: MED 272A, ME368A, OIT 384 or BIOE 374A.
Terms: Spr | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)

ME 369: Cracks, Dislocations, and Waves

The 6-dimensional formalism of A. N. Stroh will be developed to treat two-dimensional problems in elastically anisotropic media. Stress fields of straight dislocations will be developed, from which the elastic fields of line cracks (treated as continuous distributions of straight dislocations) will be obtained along with stress intensity factors and energy release rates. Steady waves including plane waves, Rayleigh waves, and Stoneley waves will be treated along with problems of reflection and refraction of incident plane waves in joined anisotropic half-spaces. Anisotropic boundary element methods will be discussed. Assignments will include both analytical and semi-analytical work as well as simple numerical methods to implement Stroh's formalism. Class notes and readings will be provided.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Barnett, D. (PI)

ME 370A: Energy Systems I: Thermodynamics

Thermodynamic analysis of energy systems emphasizing systematic methodology for and application of basic principles to generate quantitative understanding. Availability, mixtures, reacting systems, phase equilibrium, chemical availability, and modern computational methods for analysis. Prerequisites: undergraduate engineering thermodynamics and computer skills such as Matlab.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bowman, C. (PI)

ME 370B: Energy Systems II: Modeling and Advanced Concepts

Development of quantitative device models for complex energy systems, including fuel cells, reformers, combustion engines, and electrolyzers, using thermodynamic and transport analysis. Student groups work on energy systems to develop conceptual understanding, and high-level, quantitative and refined models. Advanced topics in thermodynamics and special topics associated with devices under study. Prerequisite: 370A.
Terms: Win | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Edwards, C. (PI)

ME 371: Combustion Fundamentals

Heat of reaction, adiabatic flame temperature, and chemical composition of products of combustion; kinetics of combustion and pollutant formation reactions; conservation equations for multi-component reacting flows; propagation of laminar premixed flames and detonations. Prerequisite: 362A or 370A, or consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Zheng, X. (PI)

ME 372: Combustion Applications

The role of chemical and physical processes in combustion; ignition, flammability, and quenching of combustible gas mixtures; premixed turbulent flames; laminar and turbulent diffusion flames; combustion of fuel droplets and sprays. Prerequisite: 371.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Zheng, X. (PI)

ME 373: Nanomaterials Synthesis and Applications for Mechanical Engineers

This course provides an introduction to both combustion synthesis of functional nanomaterials and nanotechnology. The first part of the course will introduce basic principles, synthesis/fabrication techniques and application of nanoscience and nanotechnology. The second part of the course will discuss combustion synthesis of nanostructures in zero-, one- two- and three- dimensions, their characterization methods, physical and chemical properties, and applications in energy conversion systems.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Zheng, X. (PI)

ME 375A: StoryViz: Storytelling and Visual

StoryViz is about creating authentic & compelling communication in many media: this year's topics include sketching, video, visual design & performance. Fantastic guests and a bevy of assignments will prepare students to communicate their work and ideas genuinely, concisely, and with a keen sense of wit. Limited enrollment; application required; see http://dschool.stanford.edu/classes. Please see notes.
Terms: Aut | Units: 2-3 | Grading: Satisfactory/No Credit
Instructors: Doorley, S. (PI); Witthoft, S. (PI)

ME 377: Design Thinking Bootcamp: Experiences in Innovation and Design

Bootcamp is a fast-paced immersive experience in design thinking. You'll progress through four full cycles of the process, working with a diverse team to solve real world challenges. Field work and deep collaboration with teammates are required of all students. Tenets of design thinking including being human-centered, prototype-driven, and mindful of process. Topics include design processes, innovation methodologies, need finding, human factors, visualization, rapid prototyping, team dynamics, storytelling, and project leadership. Limited enrollment. APPLICATION REQUIRED by 9/19/12. See http://bit.ly/dbootcamp.
Terms: Aut | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Janka, D. (PI); Rogers, M. (PI); Wilson, M. (PI)

ME 378: Tell, Make, Engage: Action Stories for Entrepreneuring

Guest discussion leaders with entrepreneuring experience give the course an evolving framework of evaluative methods, formed and reformed by collaborative development within the class. Stories attached to an idea or a discovery, are considered through practice exercises, artifacts, design challenges, short papers, and presentations.
Terms: Win, Spr | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Karanian, B. (PI)

ME 380: Collaborating with the Future: Launching Large Scale Sustainable Transformations (ENVRES 380, PSYCH 380)

This project-based d.school class combines Design Thinking Processes, Behavioral Sciences, and elements of Diffusion Theory. Tools and theories introduced in class will be used to structure large-scale transformations that simultaneously create value on environmental, societal, and economic fronts. We encourage students to use this class as a launching pad for real initiatives. Primarily meant for Graduate Students. (Especially qualified/motivated Seniors will be considered). Admission to the class is through an application process which ends on March 3. Please find instructions and applications at https://dschool.stanford.edu/groups/largetransformations/.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Ambady, N. (PI); Banerjee, S. (PI); Staff, 1. (PI)

ME 381: Orthopaedic Bioengineering (BIOE 381)

Engineering approaches applied to the musculoskeletal system in the context of surgical and medical care. Fundamental anatomy and physiology. Material and structural characteristics of hard and soft connective tissues and organ systems, and the role of mechanics in normal development and pathogenesis. Engineering methods used in the evaluation and planning of orthopaedic procedures, surgery, and devices.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 382: Biomedical Engineering in Research and Development

This project based course will cover the application of engineering methods to real world biomedical problems ranging from translational biomedical research to medical device design. Topics will include the emerging importance of preventative strategies, and the biomedical challenges of an aging population. A key element of the course will be the identification of the underlying scientific principles (computational and/or experimental) for solving biomedical problems. The students will gain experience in the formation of project teams; interdisciplinary communication skills; forming testable hypothesis with biological, anatomical, and physiological considerations; testing standards for medical devices; regulatory issues; and intellectual property.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Andriacchi, T. (PI)

ME 382A: Biomedical Engineering in Research and Development

This project based course will cover the application of engineering methods to real world biomedical problems ranging from translational biomedical research to medical device design. Topics will include the emerging importance of preventative strategies, and the biomedical challenges of an aging population. A key element of the course will be the identification of the underlying scientific principles (computational and/or experimental) for solving biomedical problems. The students will gain experience in the formation of project teams; interdisciplinary communication skills; forming testable hypothesis with biological, anatomical, and physiological considerations; testing standards for medical devices; regulatory issues; and intellectual property.
Terms: not given this year | Units: 4 | Grading: Letter (ABCD/NP)

ME 382B: Medical Device Design

Continuation of the projects from 382A. With the assistance of faculty and expert consultants, students finalize research projects or device designs. Strategies for funding biomedical research and new medical ventures will also be covered.
Terms: alternate years, given next year | Units: 4 | Grading: Letter or Credit/No Credit

ME 385: Tissue Engineering Lab

Hands-on experience in the fabrication of living engineered tissues. Techniques include sterile technique, culture of mammalian cells, creation of cell-seeded scaffolds, and the effects of mechanical loading on the metabolism of living engineered tissues. Theory, background, and practical demonstration for each technique. Lab.
Terms: not given this year | Units: 1-2 | Grading: Letter or Credit/No Credit

ME 387: Soft Tissue Mechanics

Structure/function relationships and mechanical properties of soft tissues, including nonlinear elasticity, viscoelasticity, and poroelasticity.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 388: Transport Modeling for Biological Systems

Introduction to electric fields, fluid flows, transport phenomena and their application to biological systems. Maxwell's equations, electrostatics, electro-chemical-mechanical driving forces in physiological systems. Ionic diffusion in electrolytes and membrane transport. Fluid and solid continua theory for porous, hydrated biological tissues. Applications include ionic and molecular transport in tissues and cells, electrophoresis, electromechanical and physicochemical interactions in cells and the extracellular matrix of connective tissue.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 389: Biomechanical Research Symposium

Guest speakers present contemporary research on experimental and theoretical aspects of biomechanical engineering and bioengineering. May be repeated for credit.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Levenston, M. (PI)

ME 390: Thermosciences Research Project Seminar

Review of work in a particular research program and presentations of other related work.
Terms: not given this year | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 390A: High Temperature Gasdynamics Laboratory Research Project Seminar

Review of work in a particular research program and presentations of other related work.
Terms: Aut, Win, Spr | Units: 1 | Grading: Satisfactory/No Credit

ME 391: Engineering Problems

Directed study for graduate engineering students on subjects of mutual interest to student and staff member. May be used to prepare for experimental research during a later quarter under 392. Faculty sponsor required.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 392: Experimental Investigation of Engineering Problems

Graduate engineering students undertake experimental investigation under guidance of staff member. Previous work under 391 may be required to provide background for experimental program. Faculty sponsor required.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 395: Seminar in Solid Mechanics

Required of Ph.D. candidates in solid mechanics. Guest speakers present research topics related to mechanics theory, computational methods, and applications in science and engineering. May be repeated for credit. See http://mc.stanford.edu.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 396: Design and Manufacturing Forum (ME 196)

Invited speakers address issues of interest to design and manufacturing engineering and business students. Sponsored by the Product Realization Laboratory at Stanford.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Reis, R. (PI)

ME 397: Design Theory and Methodology Seminar

What do designers do when they do design? How can their performance be improved? Topics change each quarter. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Leifer, L. (PI)

ME 399: Fuel Cell Seminar

Interdisciplinary research in engineering, chemistry, and physics. Talks on fundamentals of fuel cells by speakers from Stanford, other academic and research institutions, and industry. The potential to provide high efficiency and zero emissions energy conversion for transportation and electrical power generation.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

ME 400: Thesis (Engineer Degree)

Investigation of some engineering problems. Required of Engineer degree candidates
Terms: Aut, Win, Spr, Sum | Units: 2-15 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 405: Asymptotic Methods in Computational Engineering

This course is not a standard teaching of asymptotic methods as thought in the applied math programs. Nor does it involve such elaborate algebra and analytical derivations. Instead, the class relies on students¿ numerical programing skills and introduces improvements on numerical methods using standard asymptotic and scaling ideas. The main objective of the course is to bring physical insight into numerical programming. Majority of the problems to be explored involve one-¬ and two-dimensional transient partial differential equations. Topics include: 1¿Review of numerical discretization and numerical stability, 2-Implicit versus explicit methods, 3-Introduction to regular and singular perturbation problems, 4¬¿Method of matched asymptotic expansions, 5¬¿Stationary thin interfaces: boundary layers, Debye layers,¿ 6¿Moving thin interfaces: shocks, phase-¬¿interfaces, 7-Reaction-¬diffusion problems, 8-Directional equilibrium and lubrication theory.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Mani, A. (PI)

ME 406: Turbulence Physics and Modeling Using Numerical Simulation Data

Prerequisite: consent of instructor.
Terms: Sum | Units: 2 | Grading: Letter or Credit/No Credit

ME 408: Spectral Methods in Computational Physics (CME 322)

Data analysis, spectra and correlations, sampling theorem, nonperiodic data, and windowing; spectral methods for numerical solution of partial differential equations; accuracy and computational cost; fast Fourier transform, Galerkin, collocation, and Tau methods; spectral and pseudospectral methods based on Fourier series and eigenfunctions of singular Sturm-Liouville problems; Chebyshev, Legendre, and Laguerre representations; convergence of eigenfunction expansions; discontinuities and Gibbs phenomenon; aliasing errors and control; efficient implementation of spectral methods; spectral methods for complicated domains; time differencing and numerical stability.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Moin, P. (PI)

ME 410A: Foresight and Innovation

Three quarter sequence. Learn how to develop technology-based visions and make them succeed. This course provides an intensive and hands-on approach to multiple foresight and strategy methods that teach you how to develop radical innovation. Students build an innovation model and prototype.Prerequisite: consent of instructor.
Terms: Aut | Units: 3-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Cockayne, W. (PI)

ME 410B: Foresight and Innovation

Continuation of ME410A. With model prototype in hand, students have the opportunity to further develop their innovation.
Terms: Win | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Cockayne, W. (PI)

ME 410C: Foresight and Innovation

Continuation of ME410B. With model prototype in hand, students have the opportunity to further develop their innovation.
Terms: Spr | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Cockayne, W. (PI)

ME 411: Advanced Topics in Computational Solid Mechanics

Discussion of the use of computational simulation methods for analyzing and optimizing production processes and for developing new products, based on real industrial applications in the metal forming industry. Brief review of linear and nonlinear continuum mechanics and the use of finite element methods to model solid mechanics problems, constitutive relations for metals, coupled thermo-elasto-plastic (viscoplastic) problems, modeling metal productions processes: bulk metal forming processes using rigid/viscoplastic material models, application examples: hot rolling of plates and the Mannesmann piercing processes and modeling the service behavior of steel pipes. Prerequisites: ME 338A, ME 335A,B,C, or consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 412: Engineering Functional Analysis and Finite Elements (CME 356)

Concepts in functional analysis to understand models and methods used in simulation and design. Topology, measure, and integration theory to introduce Sobolev spaces. Convergence analysis of finite elements for the generalized Poisson problem. Extensions to convection-diffusion-reaction equations and elasticity. Upwinding. Mixed methods and LBB conditions. Analysis of nonlinear and evolution problems. Prerequisites: 335A,B, CME 200, CME 204, or consent of instructor. Recommended: 333, MATH 171.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lew, A. (PI)

ME 414: Solid State Physics Issues for Mechanical Engineering Experiments

Introductory overview of principles of statistical mechanics, quantum mechanics and solid-state physics. Provides graduate Mechanical Engineering students with the understanding needed to work on devices or technologies which rely on solid-state physics. (Alternate years, not offered summer 2012).
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kenny, T. (PI)

ME 417: Total Product Integration Engineering

For students aspiring to be product development executives and leaders in research and education. Advanced methods and tools beyond the material covered in 217: quality design across global supply chain, robust product architecture for market variety and technology advances, product development risk management. Small teams or individuals conduct a practical project that produces a case study or enhancement to produce development methods and tools. Enrollment limited to 12. Prerequisites: 317A,B.
Terms: Aut | Units: 4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Beiter, K. (PI)

ME 420: Applied Electrochemistry at Micro- and Nanoscale

The class is an introduction to applied electrochemistry with focus on micro- and nanoscale applications. Basic concepts of physical chemistry are presented, of which the fundamentals of electrochemistry are built. Theory of electrochemical methods for material analyses and material modifications are discussed with emphasis on the scaling behaviors. This year electrochemical energy generation/storage devices with focus on batteries will be discussed in class. Journals articles are reviewed within the framework of the course with focus on current problems and needs in and energy conversion and storage.
Terms: Sum | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Fasching, R. (PI)

ME 421: European Entrepreneurship and Innovation Thought Leaders Seminar

Lessons from real-world experiences and challenges in European startups, corporations, universities, non-profit research institutes and venture finance organizations. Speakers include entrepreneurs, leaders from global technology companies, university researchers, venture capitalists, legal experts, senior policy makers and other guests from selected European countries and regions. Geographic scope encompasses Ireland to Russia, and Scandinavia to the Mediterranean region. Enrollment open to undergraduates and graduates in any school or department at Stanford.
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 423: D.HEALTH: Design Thinking for Health

In the U.S., 75% of medical expenditures are for illnesses that are lifestyle related such as diabetes and heart disease. If patients could change their lifestyles, medical problems could be avoided and a healthier and happier life achieved. Class employs design thinking in teams. Individual projects and small and large team projects with multiple milestones. Students work in the field, and present in class. Design Institute class; see http://dschool.stanford.edu.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)

ME 429: COMMERCIAL MEMS DESIGN

This course, taught by Dr. Gary O'Brien of the Bosch RTC, will provide insight into the issues and challenges in designing MEMS device for commercial and automotive applications. Topics to be covered in the class will include device simulation and design, design of experiments, compensation for cross-wafer and wafer-to-wafer fabrication variations, design for extreme environments, analysis and management of reliability issues including package stress, shock, drift, cost analysis of manufacturing processes, and some discussion of the unique challenges for consumer and automotive customers and markets. Student teams will develop a device design, fabrication process, and manufacturing analysis in response to a specification.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: O'Brien, G. (PI)

ME 450: Advances in Biotechnology

Guest academic and industrial speakers. Latest developments in fields such as bioenergy, green process technology, production of industrial chemicals from renewable resources, protein pharmaceutical production, industrial enzyme production, stem cell applications, medical diagnostics, and medical imaging. Biotechnology ethics, business and patenting issues, and entrepreneurship in biotechnology.
Terms: not given next year | Units: 3 | Grading: Letter or Credit/No Credit

ME 451B: Advanced Fluid Mechanics

Waves in fluids: surface waves, internal waves, inertial and acoustic waves, dispersion and group velocity, wave trains, transport due to waves, propagation in slowly varying medium, wave steepening, solitons and solitary waves, shock waves. Instability of fluid motion: dynamical systems, bifurcations, Kelvin-Helmholtz instability, Rayleigh-Benard convection, energy method, global stability, linear stability of parallel flows, necessary and sufficient conditions for stability, viscosity as a destabilizing factor, convective and absolute instability. Focus is on flow instabilities. Prerequisites: graduate courses in compressible and viscous flow.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lele, S. (PI)

ME 451C: Advanced Fluid Mechanics

Compressible flow: governing equations, Crocco-Vazsonyi¿s equations, creation and destruction of vorticity by compressibility effects, shock waves. Modal decomposition of compressible flow, linear and nonlinear modal interactions, interaction of turbulence with shock waves. Energetics of compressible turbulence, effects of compressibility on free-shear flows, turbulent boundary layers, Van Direst transformation, recovery temperature, and shock/boundary layer interaction. Strong Reynolds analogy, modeling compressible turbulent flows. Prerequisites: 355, 361A, or equivalents.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 451D: Microhydrodynamics (CHEMENG 310)

Transport phenomena on small-length scales appropriate to applications in microfluidics, complex fluids, and biology. The basic equations of mass, momentum, and energy, derived for incompressible fluids and simplified to the slow-flow limit. Topics: solution techniques utilizing expansions of harmonic and Green's functions; singularity solutions; flows involving rigid particles and fluid droplets; applications to suspensions; lubrication theory for flows in confined geometries; slender body theory; and capillarity and wetting. Prerequisites: 120A,B, 300, or equivalents.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fuller, G. (PI)

ME 453A: Finite Element-Based Modeling and Simulation of Linear Fluid/Structure Interaction Problems

Basic physics behind many fluid/structure interaction phenomena. Finite element-based computational approaches for linear modeling and simulation in the frequency domain. Vibrations of elastic structures. Linearized equations of small movements of inviscid fluids. Sloshing modes. Hydroelastic vibrations. Acoustic cavity modes. Structural-acoustic vibrations. Applications to liquid containers and underwater signatures. Prerequisite: graduate course in the finite element method or consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 453B: Computational Fluid Dynamics Based Modeling of Nonlinear Fluid/Structure Interaction Problems

Basic physics behind many high-speed flow/structure interaction phenomena. Modern computational approaches for nonlinear modeling and simulation in the time domain. Dynamic equilibrium of restrained and unrestrained elastic structures. Corotational formulation for large structural displacements and rotations. Arbitrary Lagrangian-Eulerian description of inviscid and viscous flows. Time-accurate CFD on moving and deforming grids. Discrete geometric conservation laws. Discretization of transmission conditions on non-matching discrete fluid/structure interfaces. Coupled fluid/mesh-motion/structure time integration schemes. Application to divergence, flutter, and buffeting. Prerequisites: graduate course in the finite element method, and in computational fluid dynamics.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 455: Complex Fluids and Non-Newtonian Flows (CHEMENG 462)

Definition of a complex liquid and microrheology. Division of complex fluids into suspensions, solutions, and melts. Suspensions as colloidal and non-colloidal. Extra stress and relation to the stresslet. Suspension rheology including Brownian and non-Brownian fibers. Microhydrodynamics and the Fokker-Planck equation. Linear viscoelasticity and the weak flow limit. Polymer solutions including single mode (dumbbell) and multimode models. Nonlinear viscoelasticity. Intermolecular effects in nondilute solutions and melts and the concept of reptation. Prerequisites: low Reynolds number hydrodynamics or consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

ME 457: Fluid Flow in Microdevices

Physico-chemical hydrodynamics. Creeping flow, electric double layers, and electrochemical transport such as Nernst-Planck equation; hydrodynamics of solutions of charged and uncharged particles. Device applications include microsystems that perform capillary electrophoresis, drug dispension, and hybridization assays. Emphasis is on bioanalytical applications where electrophoresis, electro-osmosis, and diffusion are important. Prerequisite: consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Santiago, J. (PI)

ME 458: Advanced Topics in Electrokinetics

Electrokinetic theory and electrokinetic separation assays. Electroneutrality approximation and weak electrolyte electrophoresis theory. Capillary zone electrophoresis, field amplified sample stacking, isoelectric focusing, and isotachophoresis. Introduction to general electrohydrodynamics (EHD) theory including the leaky dielectric concept, the Ohmic model formulation, and electrokinetic flow instabilities. Prerequisite: ME 457.
Terms: not given this year | Units: 3-5 | Grading: Letter (ABCD/NP)

ME 463: Advanced Topics in Plasma Science and Engineering

Research areas such as plasma diagnostics, plasma transport, waves and instabilities, and engineering applications.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 468: Experimental Research in Advanced User Interfaces (COMM 168, COMM 268, COMM 368)

Project-based course involves small (3-4) person teams going through all parts of the experimental process: question generation, experiment design, running, and data analysis. Each team creates an original, publishable project that represents a contribution to the research and practicum literatures. All experiments involve interaction between people and technology, including cars, mobile phones, websites, etc. Prerequisite: consent of instructor.
Terms: Aut, Win | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Nass, C. (PI)

ME 469: Computational Methods in Fluid Mechanics

The last two decades have seen the widespread use of Computational Fluid Dynamics (CFD) for analysis and design of thermal-fluids systems in a wide variety of engineering fields. Numerical methods used in CFD have reached a high degree of sophistication and accuracy. The objective of this course is to introduce ¿classical¿ approaches and algorithms used for the numerical simulations of incompressible flows. In addition, some of the more recent developments are described, in particular as they pertain to unstructured meshes and parallel computers. An in-depth analysis of the procedures required to certify numerical codes and results will conclude the course.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Iaccarino, G. (PI)

ME 469B: Computational Methods in Fluid Mechanics

Advanced CFD codes. Geometry modeling, CAD-CFD conversion. Structured and unstructured mesh generation. Solution methods for steady and unsteady incompressible Navier-Stokes equations. Turbulence modeling. Conjugate (solid/fluid) heat transfer problems. Development of customized physical models. Batch execution for parametric studies. Final project involving solution of a problem of student¿s choosing. Prerequisite: ME 300C/CME 206.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 470: Uncertainty Quantification

Uncertainty analysis in computational science. Probabilistic data representation, propagation techniques and validation under uncertainty. Mathematical and statistical foundations of random variables and processes for uncertainty modeling. Focus is on state-of-the-art propagation schemes, sampling techniques, and stochastic Galerkin methods. The concept of model validation under uncertainty and the determination of confidence bounds estimates. Prerequisite: basic probability and statistics at the level of CME 106 or equivalent.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Iaccarino, G. (PI)

ME 471: Turbulent Combustion

Basis of turbulent combustion models. Assumption of scale separation between turbulence and combustion, resulting in Reynolds number independence of combustion models. Level-set approach for premixed combustion. Different regimes of premixed turbulent combustion with either kinematic or diffusive flow/chemistry interaction leading to different scaling laws and unified expression for turbulent velocity in both regimes. Models for non-premixed turbulent combustion based on mixture fraction concept. Analytical predictions for flame length of turbulent jets and NOx formation. Partially premixed combustion. Analytical scaling for lift-off heights of lifted diffusion.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Urzay Lobo, J. (PI)

ME 484: Computational Methods in Cardiovascular Bioengineering (BIOE 484)

Lumped parameter, one-dimensional nonlinear and linear wave propagation, and three-dimensional modeling techniques applied to simulate blood flow in the cardiovascular system and evaluate the performance of cardiovascular devices. Construction of anatomic models and extraction of physiologic quantities from medical imaging data. Problems in blood flow within the context of disease research, device design, and surgical planning.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

ME 485: Modeling and Simulation of Human Movement (BIOE 485)

Direct experience with the computational tools used to create simulations of human movement. Lecture/labs on animation of movement; kinematic models of joints; forward dynamic simulation; computational models of muscles, tendons, and ligaments; creation of models from medical images; control of dynamic simulations; collision detection and contact models. Prerequisite: 281, 331A,B, or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Delp, S. (PI)

ME 491: Ph.D. Teaching Experience

Required of Ph.D. students. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 3 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 492: Mechanical Engineering Teaching Assistance Training

Terms: Aut, Win, Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Edwards, C. (PI); Ferguson, P. (PI); Gardella, I. (PI)

ME 495A: ME Seminar Series: Theoretical and Computational Fluid Dynamics

Seminars will feature early career mechanical engineers working on leading edge problems in theoretical and computational fluid mechanics and related disciplines. Guest speakers will come from top universities within the U.S. and internationally and will discuss both their past research and plans for building a research program in the future.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Eaton, J. (PI)

ME 495B: ME Seminar Series: At the Interface between Mechanical Engineering and Biology

Seminars will feature early career mechanical engineers working on leading edge problems in biomechanical engineering. Topics include mechanobiology, cell mechanics, transport phenomena in biological systems, bio-inspired design, design and analysis of biodevices or bioinstrumentation, biomaterials, and modeling of physiological systems. Guest speakers will come from top universities within the U.S. and internationally, and will discuss both their past research and plans for building a research program in the future.
Terms: Win, Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Cutkosky, M. (PI); Staff, 1. (PI)

ME 500: Thesis (Ph.D.)

Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit

ME 571: Surgical Robotics Seminar (CS 571)

Surgical robots developed and implemented clinically on varying scales. Seminar goal is to expose students from engineering, medicine, and business to guest lecturers from academia and industry.engineering and clinical aspects connected to design and use of surgical robots, varying in degree of complexity and procedural role. May be repeated for credit.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Barbagli, F. (PI); Okamura, A. (PI)

ME 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

ME 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

COMM 1A: Media Technologies, People, and Society (COMM 211)

(Graduate students register for COMM 211.) Open to non-majors. Introduction to the concepts and contexts of communication. A topics-structured orientation emphasizing the field and the scholarly endeavors represented in the department.
Terms: Aut | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Nass, C. (PI)

COMM 1B: Media, Culture, and Society (AMSTUD 1B)

The institutions and practices of mass media, including television, film, radio, and digital media, and their role in shaping culture and social life. The media's shifting relationships to politics, commerce, and identity.
Terms: Win | Units: 5 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Turner, F. (PI)

COMM 103S: Media Entertainment

The impact of media entertainment on individuals, social groups, and societies. Sources include a diverse cross-section of entertainment. Introduction to psychological and socio-psychological theories. Empirical findings relating to media entertainment as a stimulus and a reception phenomenon. What renders diverse genres of media content and format enjoyable? Why do individuals pursue entertainment experiences in ever-increasing numbers? What is the political impact of apolitical media entertainment?
Terms: Sum | Units: 3-5 | Grading: Letter or Credit/No Credit
Instructors: Bosshart, L. (PI)

COMM 104W: Reporting, Writing, and Understanding the News

Techniques of news reporting and writing. The value and role of news in democratic societies. Gateway class to journalism. Prerequisite for all COMM 177/277 classes. Limited enrollment. Preference to sophomores and juniors.
Terms: Aut, Win, Spr | Units: 5 | Grading: Letter (ABCD/NP)
Instructors: Zacharia, J. (PI)

COMM 106: Communication Research Methods (COMM 206)

(Graduate students register for COMM 206.) Conceptual and practical concerns underlying commonly used quantitative approaches, including experimental, survey, content analysis, and field research in communication. Pre- or corequisite: STATS 60 or consent of instructor.
Terms: Aut | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Voelker, D. (PI)

COMM 106S: Communication Research Methods

An introduction to social science research methods for those who have little or no prior experience in statistics. Designed to provide students with a critical framework and a set of tools to examine social problems - especially those related to the area of communication and the media. Students will be guided through the process of formulating real-world research questions, parsing them into analyzable statements, engaging in systematic data collection and analysis, and finally, thinking about value and limits of its outcome. Hands-on research experience provided.
Terms: not given this year | Units: 3-5 | Grading: Letter or Credit/No Credit

COMM 108: Media Processes and Effects (COMM 208)

(Graduate students register for COMM 208.) The process of communication theory construction including a survey of social science paradigms and major theories of communication. Recommended: 1 or PSYCH 1.
Terms: Spr | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Bailenson, J. (PI)

COMM 115S: Fun & Games: Motivational Design of User Experiences

Various interventions are employing virtual rewards, teams, and badges to incentivize real world behavior ranging from commercial purchases to reductions in home energy use. These are examples of motivational design, in which the engaging qualities common to games and other enjoyable activities are leveraged to drive particular behaviors. Using scientific research and industry examples we will examine the key processes and concepts that make up such designs. Along the way we will compare different theoretical approaches to motivation, consider the potential application of emerging technologies for new motivational designs, and discuss the ethics of designing for behavior change.
Terms: not given this year | Units: 3-5 | Grading: Letter or Credit/No Credit

COMM 116: Journalism Law (COMM 216)

(Graduate students register for 216.) Laws and regulation impacting journalists. Topics include libel, privacy, news gathering, protection sources, fair trial and free press, theories of the First Amendment, and broadcast regulation. Prerequisite: Journalism M.A. student or advanced Communication major.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Wheaton, J. (PI)

COMM 117: Digital Journalism (COMM 217)

(Graduate students register for COMM 217.) Seminar and practicum. The implications of new media for journalists. Professional and social issues related to the web as a case of new media deployment, as a story, as a research and reporting tool, and as a publishing channel. Prerequisite: Journalism M.A. student or consent of instructor.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Brenner, R. (PI)

COMM 118S: Entrepreneurial Communication (COMM 218S)

New business ventures are often incubated on college campuses. What makes the difference between a successful and unsuccessful entrepreneur-communication. Specifically, the entrepreneur's ability to communicate their vision to potential investors, employees, and customers. This seminar will explore successful and unsuccessful entrepreneurial communication. Students will learn the basics of persuasive oral and written communication, and then apply these principles to their own ideas. This course will help you to develop confidence in your speaking and writing as an entrepreneur through presentations and assignments, lectures and discussions, guest speakers, simulated activities, and video recorded feedback. In this course you will learn to: - Create communication strategies at an individual and organizational level - Develop clearly organized and effective presentations and documents - Diagnose and expand your personal writing and oral delivery style - Adapt your delivery style to different material and audiences - Enhance oral delivery through effective visual aids
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

COMM 120W: Digital Media in Society (AMSTUD 120, COMM 220)

(Graduate students register for 220.) Contemporary debates concerning the social and cultural impact of digital media. Topics include the historical origins of digital media, cultural contexts of their development and use, and influence of digital media on conceptions of self, community, and state. Priority to Juniors and Seniors.
Terms: Aut | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Turner, F. (PI)

COMM 122: Content Analysis: Studying Communication Artifacts (COMM 222)

An empirical and systematic investigation of documented messages in print, graphical, and audio-visual forms and observed human communication behaviors. Focuses on the design and execution of content analytic studies, including manifest vs. latent content, measurement issues, reliability and validity assessment, computer text analysis, and traditional human-coder techniques. Prerequisite: junior, senior or grad standing; COMM 106/206 or an equivalent course in basic social science research. Limited enrollment; preference to doctoral students.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Voelker, D. (PI)

COMM 123: Argumentation and Persuasion (COMM 223)

We all know that appeals based on logic and sound evidence often fail where less rational appeals that "shouldn't" work, succeed. This course examines persuasion, the influencing of attitudes, beliefs or behavior, and locates within that broad subject argumentation, the process of reasoning methodically from evidence. Argumentation, the socially acceptable method of persuasion, typically confines itself to the rules of logic and has as its goal the recognition of states and causal relationships held by the arguer to objectively exist. Other methods of persuasion can succeed while flouting those rules, but only within limits, as the story of the Emperor's New Clothes reminds us. This course will explore whether those limits be accounted for by the capacity limitations and heuristics and biases of human information processing. Topics to be covered include evolutionary explanations; the central and peripheral routes to persuasion; source, channel and receiver factors; attitude-behavior consistency; the roles of involvement, elaboration, affect and social influence; critical thinking skills and logical fallacies. Limited enrollment; preference to juniors, seniors and graduate students, and within these, to Communication majors.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Voelker, D. (PI)

COMM 125: Perspectives on American Journalism (COMM 225)

(Graduate students register for COMM 225.) An examination of the practice of American journalism, focusing on the political, social, cultural, economic and technological forces that have shaped the U. S. press since the early 1800s. Aimed at consumers as well as producers of news, the objective of this course is to provide a framework and vocabulary for judging the value and quality of everyday journalism.
Terms: Aut | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Glasser, T. (PI)

COMM 130N: The idea of a free press

Preference to freshmen. An examination of the meaning of freedom of the press, tied to but not bound by various Supreme Court rulings on the scope and purpose of the First Amendment's speech and press clauses. Discussions will include a look at the recent and rapid computerization of communication and what it portends for the future of a free press.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Glasser, T. (PI)

COMM 131: Media Ethics and Responsibility (COMM 231)

(Graduate students register for COMM 231.) The development of professionalism among American journalists, emphasizing the emergence of objectivity as a professional and the epistemological norm. An applied ethics course where questions of power, freedom, and truth autonomy are treated normatively so as to foster critical thinking about the origins and implications of commonly accepted standards of responsible journalism.
Terms: not given this year | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)

COMM 133: Need to Know: The Tension between a Free Press and National Security Decision Making (COMM 233)

The course will examine the dynamic interaction at the highest levels of government and the media when news coverage of secret national security policy and operations impinges on United States defense, diplomatic and intelligence decision making. Attitudes, practices and actions by the media and the government will be explored through a series of case studies and simulations. Former editors, reporters and government officials will appear as guest speakers. The goal of the course is to inform students about the vital but often fraught relationship between a free press and the government in a democratic society, especially in the management of national security affairs. And to give students background and experience in how to weigh clashing interests and make enlightened decisions that serve the public and national interest. Preference to juniors, seniors, graduate students.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Taubman, P. (PI)

COMM 134: Public Participation and Public Policy (COMM 234)

Examines the role of public participation in public policy making. Around the world, policymakers seek to engage their publics. But, even though public participation is important, it is also problematic. Public meetings can become dysfunctional and turn into media spectacles instead of actually gathering the opinions of the public. The question becomes, when and how should the public be consulted in order to effectively impact public policies? There are consequences of engaging the public, and this seminar explores the methods used to engage publics around the world.
Terms: not given this year | Units: 4-5 | Grading: Letter or Credit/No Credit

COMM 135: Deliberative Democracy and its Critics (AMSTUD 135, COMM 235, COMM 335, POLISCI 234P, POLISCI 334P)

This course examines the theory and practice of deliberative democracy and engages both in a dialogue with critics. Can a democracy which emphasizes people thinking and talking together on the basis of good information be made practical in the modern age? What kinds of distortions arise when people try to discuss politics or policy together? The course draws on ideas of deliberation from Madison and Mill to Rawls and Habermas as well as criticisms from the jury literature, from the psychology of group processes and from the most recent normative and empirical literature on deliberative forums. Deliberative Polling, its applications, defenders and critics, both normative and empirical, will provide a key case for discussion.
Terms: Spr | Units: 3-5 | Grading: Letter or Credit/No Credit
Instructors: Fishkin, J. (PI); Siu, A. (PI)

COMM 137W: The Dialogue of Democracy (AMSTUD 137, COMM 237, POLISCI 232T, POLISCI 332T)

All forms of democracy require some kind of communication so people can be aware of issues and make decisions. This course looks at competing visions of what democracy should be and different notions of the role of dialogue in a democracy. Is it just campaigning or does it include deliberation? Small scale discussions or sound bites on television? Or social media? What is the role of technology in changing our democratic practices, to mobilize, to persuade, to solve public problems? This course will include readings from political theory about democratic ideals - from the American founders to J.S. Mill and the Progressives to Joseph Schumpeter and modern writers skeptical of the public will. It will also include contemporary examinations of the media and the internet to see how those practices are changing and how the ideals can or cannot be realized.
Terms: Win | Units: 4-5 | UG Reqs: GER:ECEthicReas | Grading: Letter or Credit/No Credit
Instructors: Fishkin, J. (PI)

COMM 140: Digital Media Entrepreneurship (COMM 240)

(Graduate students register for COMM 240.) Primarily for graduate journalism and computer science students. Silicon Valley's new media culture, digital storytelling skills and techniques, web-based skills, and entrepreneurial ventures. Guest speakers.
Terms: Spr | Units: 3-5 | Grading: Letter (ABCD/NP)
Instructors: Grimes, A. (PI)

COMM 147: Modern History and Future of Journalism (COMM 247)

(Graduate students register for COMM 247.) The birth and evolution of local and national television news. The modern history of newspapers. Can they survive in the era of online journalism?
Terms: Spr | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Brinkley, J. (PI)

COMM 160: The Press and the Political Process (COMM 260, POLISCI 323R)

(Graduate students register for COMM 260.) The role of mass media and other channels of communication in political and electoral processes.
Terms: not given this year | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)

COMM 162: Campaigns, Voting, Media, and Elections (COMM 262, POLISCI 120B)

This course examines the theory and practice of American campaigns and elections. First, we will attempt to explain the behavior of the key players -- candidates, parties, journalists, and voters -- in terms of the institutional arrangements and political incentives that confront them. Second, we will use current and recent election campaigns as "laboratories" for testing generalizations about campaign strategy and voter behavior. Third, we examine selections from the academic literature dealing with the origins of partisan identity, electoral design, and the immediate effects of campaigns on public opinion, voter turnout, and voter choice. As well, we'll explore issues of electoral reform and their more long-term consequences for governance and the political process.
Terms: Aut | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Iyengar, S. (PI)

COMM 164: The Psychology of Communication About Politics in America (COMM 264, POLISCI 224L, PSYCH 170)

Focus is on how politicians and government learn what Americans want and how the public's preferences shape government action; how surveys measure beliefs, preferences, and experiences; how poll results are criticized and interpreted; how conflict between polls is viewed by the public; how accurate surveys are and when they are accurate; how to conduct survey research to produce accurate measurements; designing questionnaires that people can understand and use comfortably; how question wording can manipulate poll results; corruption in survey research.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Krosnick, J. (PI)

COMM 165N: Cars: Past, Present, and Future

Focus on the past, present and future of the automobile, bridging the Humanities, Social Sciences, Design, and Engineering. Focus on the human experiences of designing, making, driving, being driven, living with, and dreaming of the automobile. A different theme will be featured each week in discussion around a talk and supported by key readings and media. The course is informed by history, archaeology, ethnography, human-technology interaction, mechanical engineering, and cognitive science. Preference to freshmen.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

COMM 166: Virtual People (COMM 266)

(Graduate students register for COMM 266.) The concept of virtual people or digital human representations; methods of constructing and using virtual people; methodological approaches to interactions with and among virtual people; and current applications. Viewpoints including popular culture, literature, film, engineering, behavioral science, computer science, and communication.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Bailenson, J. (PI)

COMM 167: Advanced Seminar in Virtual Reality Research

Restricted to students with previous research experience in virtual reality. Experimental methods and other issues.
Terms: not given this year | Units: 1-3 | Grading: Satisfactory/No Credit

COMM 168: Experimental Research in Advanced User Interfaces (COMM 268, COMM 368, ME 468)

Project-based course involves small (3-4) person teams going through all parts of the experimental process: question generation, experiment design, running, and data analysis. Each team creates an original, publishable project that represents a contribution to the research and practicum literatures. All experiments involve interaction between people and technology, including cars, mobile phones, websites, etc. Prerequisite: consent of instructor.
Terms: Aut, Win | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Nass, C. (PI)

COMM 169: Computers and Interfaces (COMM 269)

(Graduate students register for COMM 269.) Interdisciplinary. User responses to interfaces and design implications of those responses. Theories from different disciplines illustrate responses to textual, voice-based, pictorial, metaphoric, conversational, adaptive, agent-based, intelligent, and anthropomorphic interfaces. Group design project applying theory to the design of products or services for developing countries.
Terms: Win | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter or Credit/No Credit
Instructors: Nass, C. (PI)

COMM 171: Moving Pictures: How the Web, Mobile and Tablets are Revolutionizing Video Journalism (COMM 271)

(Graduate students register for 271.) Production of multimedia assignments for traditional news beats using audio, still photography, graphics and video. 2-hour lab class for creative, conceptual and technical skills for production of multimedia stories. Prerequesite: Journalism MA student or instructor's consent.
Terms: Win | Units: 3-5 | Grading: Letter (ABCD/NP)
Instructors: Migielicz, G. (PI)

COMM 172: Media Psychology (COMM 272)

(Graduate students register for COMM 272.) The literature related to psychological processing and the effects of media. Topics: unconscious processing; picture perception; attention and memory; emotion; the physiology of processing media; person perception; pornography; consumer behavior; advanced film and television systems; and differences among reading, watching, and listening.
Terms: Spr | Units: 4-5 | UG Reqs: GER:DBSocSci | Grading: Letter (ABCD/NP)
Instructors: Reeves, B. (PI)

COMM 176: Advanced Digital Media Production (COMM 276)

In-depth reporting and production using audio, images and video. Focus on an in-depth journalism project with appropriate uses of digital media: audio, photography, graphics, and video. Topics include advanced field techniques and approaches (audio, video, still) and emphasis on creating a non-fiction narrative arc in a multimedia piece of 10-12 minutes. Prerequisite: COMM 275 or consent of instructor
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Migielicz, G. (PI)

COMM 177C: Specialized Writing and Reporting: Environmental Journalism (COMM 277C, ENVRES 277C)

(Graduate students register for COMM / ENVRES 277C.) Practical, collaborative, writing-intensive course in science-based environmental journalism. Science and journalism students learn how to identify and write engaging stories about environmental issues and science, how to assess the quality and relevance of environmental news, how to cover the environment and science beats effectively, and how to build bridges between the worlds of journalism and science. Limited enrollment: preference to journalism students and students in the natural and environmental sciences. Prerequisite: COMM 104, ENVRES 200 or consent of instructor. Admissions by application only, available from thayden@stanford.edu and due 3/28/12.
Terms: Spr | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Hayden, T. (PI)

COMM 177D: Specialized Writing and Reporting: Magazine Journalism (COMM 277D)

(Graduate students register for COMM 277D.) How to report, write, edit, and read magazine articles, emphasizing long-form narrative. Tools and templates of story telling such as scenes, characters, dialogue, and narrative arc. How the best magazine stories defy or subvert conventional wisdom and bring fresh light to the human experience through reporting, writing, and moral passion. Prerequisite: 104 or consent of instructor.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Kahn, J. (PI)

COMM 177G: Specialized Writing and Reporting: Covering Silicon Valley (COMM 277G)

(Graduate students register for COMM 277G.) Business reporting basics in the context of Silicon Valley's technology scene. Prerequisite: 104 or consent of instructor.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Grimes, A. (PI)

COMM 177I: Specialized Writing and Reporting: Investigative Reporting (COMM 277I)

Graduate students register for COMM 277I.) Under the supervision of editors from the Center for Investigative Reporting, students will work on a group investigative project with the end-goal of publication and distribution through CIR's California Watch project. The class will emphasize the history and role of investigative reporting as well as skills and techniques needed to do it. Limited enrollment. Prerequisite: instructor consent. Go to http://comm.stanford.edu/faculty/grimes for application instructions.
Terms: not given this year | Units: 4-5 | Grading: Letter (ABCD/NP)

COMM 177S: Specialized Writing and Reporting: Sports Journalism (COMM 277S)

(Graduate students register for COMM 277S.) Workshop. An examination of American sports writing from the 1920¿s Golden Age of Sports to present. Students become practitioners of the sports writing craft in an intensive laboratory. Hones journalistic skills such as specialized reporting, interviewing, deadline writing, creation of video projects, and conceptualizing and developing stories for print and online. Prerequisite: 104 or consent of instructor.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Pomerantz, G. (PI)

COMM 177Y: Specialized Writing and Reporting: Foreign Correspondence in the Middle East and Asia (COMM 277Y)

(Graduate students register for COMM 277Y.) What's involved in working as a foreign correspondent in these important and volatile parts of the world, where in many cases journalists are not respected and may face danger -- taught by a journalist who has worked extensively in both regions. (no pre-requisites)
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Brinkley, J. (PI)

COMM 182: Virtual Communities and Social Media (COMM 282)

(Graduate students register for COMM 282.) Students will take away from this course a set of conceptual tools, a vocabulary, and an analytical framework with which to recognize, understand, and more effectively manage new social practices online, together with a familiarity with the literature regarding social media and identity, community, collective action, public sphere, social capital, networks, and social networks. Students will also develop skills at using online forums, blogs, microblogs, wikis for research, collaboration, and communication. Limited enrollment. Prerequisite: instructor consent. Go to http://comm.stanford.edu/faculty/rheingold/ for application instructions.
Terms: Aut | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Rheingold, H. (PI)

COMM 183: Social Media Literacies (COMM 283)

Today's personal, social, political, economic worlds are all affected by digital media and networked publics. Viral videos, uprisings from Tahrir to #OWS, free search engines, abundant inaccuracy and sophisticated disinformation online, indelible and searchable digital footprints, laptops in lecture halls and BlackBerries at the dinner table, twenty-something social media billionaires, massive online university courses -- it's hard to find an aspect of daily life around the world that is not being transformed by the tweets, blogs, wikis, apps, movements, likes and plusses, tags, text messages, and comments two billion Internet users and six billion mobile phone users emit. New individual and collaborative skills are emerging. This course introduces students to both the literature about and direct experience of these new literacies: research foundations and practical methods to control attention, attitudes and tools necessary for critical consumption of information, best practices of individual digital participation and collective participatory culture, the use of collaborative media and methodologies, and the application of network know-how to life online. Contrasting perspectives are offered in the readings and explored through classroom and online discussion. In each three hour class, the instructor will lecture for approximately one half hour, student project teams will present and facilitate discussion about mindmaps and the lexicon for approximately one half hour, students will engage in group activities for about an hour, and instructor will facilitate full class discussion for about an hour. Students actively collaborate and cooperate in their learning during and between classes through small group discussions and face to face exercises, forums, blogs, mindmaps and wikis. Prerequisite: Instructor consent. Go to http://socialmediaclassroom.com/host/vircom/lockedwiki/comm183 for application instructions
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Rheingold, H. (PI)

COMM 195: Honors Thesis

Qualifies students to conduct communication research. Student must apply for department honors thesis program during Spring Quarter of junior year.
Terms: Aut, Win, Spr, Sum | Units: 5 | Repeatable for credit | Grading: Letter (ABCD/NP)

COMM 199: Individual Work

For students with high academic standing. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit | Grading: Letter or Credit/No Credit

COMM 206: Communication Research Methods (COMM 106)

(Graduate students register for COMM 206.) Conceptual and practical concerns underlying commonly used quantitative approaches, including experimental, survey, content analysis, and field research in communication. Pre- or corequisite: STATS 60 or consent of instructor.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Voelker, D. (PI)

COMM 208: Media Processes and Effects (COMM 108)

(Graduate students register for COMM 208.) The process of communication theory construction including a survey of social science paradigms and major theories of communication. Recommended: 1 or PSYCH 1.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Bailenson, J. (PI)

COMM 211: Media Technologies, People, and Society (COMM 1A)

(Graduate students register for COMM 211.) Open to non-majors. Introduction to the concepts and contexts of communication. A topics-structured orientation emphasizing the field and the scholarly endeavors represented in the department.
Terms: Aut | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Nass, C. (PI)

COMM 212: Models of Democracy (COMM 312, POLISCI 237, POLISCI 337)

Ancient and modern varieties of democracy; debates about their normative and practical strengths and the pathologies to which each is subject. Focus is on participation, deliberation, representation, and elite competition, as values and political processes. Formal institutions, political rhetoric, technological change, and philosophical critique. Models tested by reference to long-term historical natural experiments such as Athens and Rome, recent large-scale political experiments such as the British Columbia Citizens' Assembly, and controlled experiments.
Terms: not given this year | Units: 3-5 | Grading: Letter or Credit/No Credit

COMM 216: Journalism Law (COMM 116)

(Graduate students register for 216.) Laws and regulation impacting journalists. Topics include libel, privacy, news gathering, protection sources, fair trial and free press, theories of the First Amendment, and broadcast regulation. Prerequisite: Journalism M.A. student or advanced Communication major.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Wheaton, J. (PI)

COMM 217: Digital Journalism (COMM 117)

(Graduate students register for COMM 217.) Seminar and practicum. The implications of new media for journalists. Professional and social issues related to the web as a case of new media deployment, as a story, as a research and reporting tool, and as a publishing channel. Prerequisite: Journalism M.A. student or consent of instructor.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Brenner, R. (PI)

COMM 218S: Entrepreneurial Communication (COMM 118S)

New business ventures are often incubated on college campuses. What makes the difference between a successful and unsuccessful entrepreneur-communication. Specifically, the entrepreneur's ability to communicate their vision to potential investors, employees, and customers. This seminar will explore successful and unsuccessful entrepreneurial communication. Students will learn the basics of persuasive oral and written communication, and then apply these principles to their own ideas. This course will help you to develop confidence in your speaking and writing as an entrepreneur through presentations and assignments, lectures and discussions, guest speakers, simulated activities, and video recorded feedback. In this course you will learn to: - Create communication strategies at an individual and organizational level - Develop clearly organized and effective presentations and documents - Diagnose and expand your personal writing and oral delivery style - Adapt your delivery style to different material and audiences - Enhance oral delivery through effective visual aids
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

COMM 220: Digital Media in Society (AMSTUD 120, COMM 120W)

(Graduate students register for 220.) Contemporary debates concerning the social and cultural impact of digital media. Topics include the historical origins of digital media, cultural contexts of their development and use, and influence of digital media on conceptions of self, community, and state. Priority to Juniors and Seniors.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Turner, F. (PI)

COMM 222: Content Analysis: Studying Communication Artifacts (COMM 122)

An empirical and systematic investigation of documented messages in print, graphical, and audio-visual forms and observed human communication behaviors. Focuses on the design and execution of content analytic studies, including manifest vs. latent content, measurement issues, reliability and validity assessment, computer text analysis, and traditional human-coder techniques. Prerequisite: junior, senior or grad standing; COMM 106/206 or an equivalent course in basic social science research. Limited enrollment; preference to doctoral students.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Voelker, D. (PI)

COMM 223: Argumentation and Persuasion (COMM 123)

We all know that appeals based on logic and sound evidence often fail where less rational appeals that "shouldn't" work, succeed. This course examines persuasion, the influencing of attitudes, beliefs or behavior, and locates within that broad subject argumentation, the process of reasoning methodically from evidence. Argumentation, the socially acceptable method of persuasion, typically confines itself to the rules of logic and has as its goal the recognition of states and causal relationships held by the arguer to objectively exist. Other methods of persuasion can succeed while flouting those rules, but only within limits, as the story of the Emperor's New Clothes reminds us. This course will explore whether those limits be accounted for by the capacity limitations and heuristics and biases of human information processing. Topics to be covered include evolutionary explanations; the central and peripheral routes to persuasion; source, channel and receiver factors; attitude-behavior consistency; the roles of involvement, elaboration, affect and social influence; critical thinking skills and logical fallacies. Limited enrollment; preference to juniors, seniors and graduate students, and within these, to Communication majors.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Voelker, D. (PI)

COMM 225: Perspectives on American Journalism (COMM 125)

(Graduate students register for COMM 225.) An examination of the practice of American journalism, focusing on the political, social, cultural, economic and technological forces that have shaped the U. S. press since the early 1800s. Aimed at consumers as well as producers of news, the objective of this course is to provide a framework and vocabulary for judging the value and quality of everyday journalism.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Glasser, T. (PI)

COMM 231: Media Ethics and Responsibility (COMM 131)

(Graduate students register for COMM 231.) The development of professionalism among American journalists, emphasizing the emergence of objectivity as a professional and the epistemological norm. An applied ethics course where questions of power, freedom, and truth autonomy are treated normatively so as to foster critical thinking about the origins and implications of commonly accepted standards of responsible journalism.
Terms: not given this year | Units: 4-5 | Grading: Letter (ABCD/NP)

COMM 233: Need to Know: The Tension between a Free Press and National Security Decision Making (COMM 133)

The course will examine the dynamic interaction at the highest levels of government and the media when news coverage of secret national security policy and operations impinges on United States defense, diplomatic and intelligence decision making. Attitudes, practices and actions by the media and the government will be explored through a series of case studies and simulations. Former editors, reporters and government officials will appear as guest speakers. The goal of the course is to inform students about the vital but often fraught relationship between a free press and the government in a democratic society, especially in the management of national security affairs. And to give students background and experience in how to weigh clashing interests and make enlightened decisions that serve the public and national interest. Preference to juniors, seniors, graduate students.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Taubman, P. (PI)

COMM 234: Public Participation and Public Policy (COMM 134)

Examines the role of public participation in public policy making. Around the world, policymakers seek to engage their publics. But, even though public participation is important, it is also problematic. Public meetings can become dysfunctional and turn into media spectacles instead of actually gathering the opinions of the public. The question becomes, when and how should the public be consulted in order to effectively impact public policies? There are consequences of engaging the public, and this seminar explores the methods used to engage publics around the world.
Terms: not given this year | Units: 4-5 | Grading: Letter or Credit/No Credit

COMM 235: Deliberative Democracy and its Critics (AMSTUD 135, COMM 135, COMM 335, POLISCI 234P, POLISCI 334P)

This course examines the theory and practice of deliberative democracy and engages both in a dialogue with critics. Can a democracy which emphasizes people thinking and talking together on the basis of good information be made practical in the modern age? What kinds of distortions arise when people try to discuss politics or policy together? The course draws on ideas of deliberation from Madison and Mill to Rawls and Habermas as well as criticisms from the jury literature, from the psychology of group processes and from the most recent normative and empirical literature on deliberative forums. Deliberative Polling, its applications, defenders and critics, both normative and empirical, will provide a key case for discussion.
Terms: Spr | Units: 3-5 | Grading: Letter or Credit/No Credit
Instructors: Fishkin, J. (PI); Siu, A. (PI)

COMM 237: The Dialogue of Democracy (AMSTUD 137, COMM 137W, POLISCI 232T, POLISCI 332T)

All forms of democracy require some kind of communication so people can be aware of issues and make decisions. This course looks at competing visions of what democracy should be and different notions of the role of dialogue in a democracy. Is it just campaigning or does it include deliberation? Small scale discussions or sound bites on television? Or social media? What is the role of technology in changing our democratic practices, to mobilize, to persuade, to solve public problems? This course will include readings from political theory about democratic ideals - from the American founders to J.S. Mill and the Progressives to Joseph Schumpeter and modern writers skeptical of the public will. It will also include contemporary examinations of the media and the internet to see how those practices are changing and how the ideals can or cannot be realized.
Terms: Win | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Fishkin, J. (PI)

COMM 240: Digital Media Entrepreneurship (COMM 140)

(Graduate students register for COMM 240.) Primarily for graduate journalism and computer science students. Silicon Valley's new media culture, digital storytelling skills and techniques, web-based skills, and entrepreneurial ventures. Guest speakers.
Terms: Spr | Units: 3-5 | Grading: Letter (ABCD/NP)
Instructors: Grimes, A. (PI)

COMM 247: Modern History and Future of Journalism (COMM 147)

(Graduate students register for COMM 247.) The birth and evolution of local and national television news. The modern history of newspapers. Can they survive in the era of online journalism?
Terms: Spr | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Brinkley, J. (PI)

COMM 260: The Press and the Political Process (COMM 160, POLISCI 323R)

(Graduate students register for COMM 260.) The role of mass media and other channels of communication in political and electoral processes.
Terms: not given this year | Units: 4-5 | Grading: Letter (ABCD/NP)

COMM 262: Campaigns, Voting, Media, and Elections (COMM 162, POLISCI 120B)

This course examines the theory and practice of American campaigns and elections. First, we will attempt to explain the behavior of the key players -- candidates, parties, journalists, and voters -- in terms of the institutional arrangements and political incentives that confront them. Second, we will use current and recent election campaigns as "laboratories" for testing generalizations about campaign strategy and voter behavior. Third, we examine selections from the academic literature dealing with the origins of partisan identity, electoral design, and the immediate effects of campaigns on public opinion, voter turnout, and voter choice. As well, we'll explore issues of electoral reform and their more long-term consequences for governance and the political process.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Iyengar, S. (PI)

COMM 264: The Psychology of Communication About Politics in America (COMM 164, POLISCI 224L, PSYCH 170)

Focus is on how politicians and government learn what Americans want and how the public's preferences shape government action; how surveys measure beliefs, preferences, and experiences; how poll results are criticized and interpreted; how conflict between polls is viewed by the public; how accurate surveys are and when they are accurate; how to conduct survey research to produce accurate measurements; designing questionnaires that people can understand and use comfortably; how question wording can manipulate poll results; corruption in survey research.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Krosnick, J. (PI)

COMM 266: Virtual People (COMM 166)

(Graduate students register for COMM 266.) The concept of virtual people or digital human representations; methods of constructing and using virtual people; methodological approaches to interactions with and among virtual people; and current applications. Viewpoints including popular culture, literature, film, engineering, behavioral science, computer science, and communication.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Bailenson, J. (PI)

COMM 268: Experimental Research in Advanced User Interfaces (COMM 168, COMM 368, ME 468)

Project-based course involves small (3-4) person teams going through all parts of the experimental process: question generation, experiment design, running, and data analysis. Each team creates an original, publishable project that represents a contribution to the research and practicum literatures. All experiments involve interaction between people and technology, including cars, mobile phones, websites, etc. Prerequisite: consent of instructor.
Terms: Aut, Win | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Nass, C. (PI)

COMM 269: Computers and Interfaces (COMM 169)

(Graduate students register for COMM 269.) Interdisciplinary. User responses to interfaces and design implications of those responses. Theories from different disciplines illustrate responses to textual, voice-based, pictorial, metaphoric, conversational, adaptive, agent-based, intelligent, and anthropomorphic interfaces. Group design project applying theory to the design of products or services for developing countries.
Terms: Win | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Nass, C. (PI)

COMM 271: Moving Pictures: How the Web, Mobile and Tablets are Revolutionizing Video Journalism (COMM 171)

(Graduate students register for 271.) Production of multimedia assignments for traditional news beats using audio, still photography, graphics and video. 2-hour lab class for creative, conceptual and technical skills for production of multimedia stories. Prerequesite: Journalism MA student or instructor's consent.
Terms: Win | Units: 3-5 | Grading: Letter (ABCD/NP)
Instructors: Migielicz, G. (PI)

COMM 272: Media Psychology (COMM 172)

(Graduate students register for COMM 272.) The literature related to psychological processing and the effects of media. Topics: unconscious processing; picture perception; attention and memory; emotion; the physiology of processing media; person perception; pornography; consumer behavior; advanced film and television systems; and differences among reading, watching, and listening.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Reeves, B. (PI)

COMM 273: Public Issues Reporting I

Reporting and writing on government and public policies and issues; their implications for the people and the press. Required for journalism M.A. students.
Terms: Aut | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Brenner, R. (PI)

COMM 274: Public Issues Reporting II

Almost everything a journalist writes about involves government, either directly or indirectly. In this course we learn about the hidden forces that control government decisions: lobbying, campaign finance, budgets and more. Students write stories and do two accompanying multimedia pieces. Prerequisites: 273, Journalism M.A. student.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Brinkley, J. (PI)

COMM 275: Multimedia Storytelling: Reporting and Production Using Audio, Still Images, and Video

Multimedia assignments coordinated with deadline reporting efforts in COMM 273 from traditional news beats using audio, still photography, and video. Use of digital audio recorders and audio production to leverage voice-over narration, interviews, and natural sound; use of digital still cameras and audio to produce audio slideshows; and the combination of these media with video in post-production with Final Cut Pro. Prerequisite: Journalism M.A. student. Corequisite: COMM 273.
Terms: Aut | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Migielicz, G. (PI)

COMM 276: Advanced Digital Media Production (COMM 176)

In-depth reporting and production using audio, images and video. Focus on an in-depth journalism project with appropriate uses of digital media: audio, photography, graphics, and video. Topics include advanced field techniques and approaches (audio, video, still) and emphasis on creating a non-fiction narrative arc in a multimedia piece of 10-12 minutes. Prerequisite: COMM 275 or consent of instructor
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Migielicz, G. (PI)

COMM 277C: Specialized Writing and Reporting: Environmental Journalism (COMM 177C, ENVRES 277C)

(Graduate students register for COMM / ENVRES 277C.) Practical, collaborative, writing-intensive course in science-based environmental journalism. Science and journalism students learn how to identify and write engaging stories about environmental issues and science, how to assess the quality and relevance of environmental news, how to cover the environment and science beats effectively, and how to build bridges between the worlds of journalism and science. Limited enrollment: preference to journalism students and students in the natural and environmental sciences. Prerequisite: COMM 104, ENVRES 200 or consent of instructor. Admissions by application only, available from thayden@stanford.edu and due 3/28/12.
Terms: Spr | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Hayden, T. (PI)

COMM 277D: Specialized Writing and Reporting: Magazine Journalism (COMM 177D)

(Graduate students register for COMM 277D.) How to report, write, edit, and read magazine articles, emphasizing long-form narrative. Tools and templates of story telling such as scenes, characters, dialogue, and narrative arc. How the best magazine stories defy or subvert conventional wisdom and bring fresh light to the human experience through reporting, writing, and moral passion. Prerequisite: 104 or consent of instructor.
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Kahn, J. (PI)

COMM 277G: Specialized Writing and Reporting: Covering Silicon Valley (COMM 177G)

(Graduate students register for COMM 277G.) Business reporting basics in the context of Silicon Valley's technology scene. Prerequisite: 104 or consent of instructor.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Grimes, A. (PI)

COMM 277I: Specialized Writing and Reporting: Investigative Reporting (COMM 177I)

Graduate students register for COMM 277I.) Under the supervision of editors from the Center for Investigative Reporting, students will work on a group investigative project with the end-goal of publication and distribution through CIR's California Watch project. The class will emphasize the history and role of investigative reporting as well as skills and techniques needed to do it. Limited enrollment. Prerequisite: instructor consent. Go to http://comm.stanford.edu/faculty/grimes for application instructions.
Terms: not given this year | Units: 4-5 | Grading: Letter (ABCD/NP)

COMM 277S: Specialized Writing and Reporting: Sports Journalism (COMM 177S)

(Graduate students register for COMM 277S.) Workshop. An examination of American sports writing from the 1920¿s Golden Age of Sports to present. Students become practitioners of the sports writing craft in an intensive laboratory. Hones journalistic skills such as specialized reporting, interviewing, deadline writing, creation of video projects, and conceptualizing and developing stories for print and online. Prerequisite: 104 or consent of instructor.
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Pomerantz, G. (PI)

COMM 277Y: Specialized Writing and Reporting: Foreign Correspondence in the Middle East and Asia (COMM 177Y)

(Graduate students register for COMM 277Y.) What's involved in working as a foreign correspondent in these important and volatile parts of the world, where in many cases journalists are not respected and may face danger -- taught by a journalist who has worked extensively in both regions. (no pre-requisites)
Terms: Aut | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Brinkley, J. (PI)

COMM 278: Journalism and Imaginative Writing in America (AMSTUD 257, ENGLISH 257)

Walt Whitman spent twenty-five years as a journalist before publishing his first book of poems. Mark Twain was a journalist for twenty years before publishing his first novel. Topics include examination of how writers¿ backgrounds in journalism shaped the poetry or fiction for which they are best known; study of recent controversies surrounding writers who blurred the line between journalism and fiction. Writers include Whitman, Fanny Fern, Twain, Pauline Hopkins, Theodore Dreiser, Charlotte Perkins Gilman, Ernest Hemingway, Meridel LeSueur.
Terms: not given this year | Units: 5 | Grading: Letter or Credit/No Credit

COMM 282: Virtual Communities and Social Media (COMM 182)

(Graduate students register for COMM 282.) Students will take away from this course a set of conceptual tools, a vocabulary, and an analytical framework with which to recognize, understand, and more effectively manage new social practices online, together with a familiarity with the literature regarding social media and identity, community, collective action, public sphere, social capital, networks, and social networks. Students will also develop skills at using online forums, blogs, microblogs, wikis for research, collaboration, and communication. Limited enrollment. Prerequisite: instructor consent. Go to http://comm.stanford.edu/faculty/rheingold/ for application instructions.
Terms: Aut | Units: 4-5 | Grading: Letter or Credit/No Credit
Instructors: Rheingold, H. (PI)

COMM 283: Social Media Literacies (COMM 183)

Today's personal, social, political, economic worlds are all affected by digital media and networked publics. Viral videos, uprisings from Tahrir to #OWS, free search engines, abundant inaccuracy and sophisticated disinformation online, indelible and searchable digital footprints, laptops in lecture halls and BlackBerries at the dinner table, twenty-something social media billionaires, massive online university courses -- it's hard to find an aspect of daily life around the world that is not being transformed by the tweets, blogs, wikis, apps, movements, likes and plusses, tags, text messages, and comments two billion Internet users and six billion mobile phone users emit. New individual and collaborative skills are emerging. This course introduces students to both the literature about and direct experience of these new literacies: research foundations and practical methods to control attention, attitudes and tools necessary for critical consumption of information, best practices of individual digital participation and collective participatory culture, the use of collaborative media and methodologies, and the application of network know-how to life online. Contrasting perspectives are offered in the readings and explored through classroom and online discussion. In each three hour class, the instructor will lecture for approximately one half hour, student project teams will present and facilitate discussion about mindmaps and the lexicon for approximately one half hour, students will engage in group activities for about an hour, and instructor will facilitate full class discussion for about an hour. Students actively collaborate and cooperate in their learning during and between classes through small group discussions and face to face exercises, forums, blogs, mindmaps and wikis. Prerequisite: Instructor consent. Go to http://socialmediaclassroom.com/host/vircom/lockedwiki/comm183 for application instructions
Terms: Win | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: Rheingold, H. (PI)

COMM 289: Journalism Master's Project

Terms: Spr | Units: 2 | Grading: Letter (ABCD/NP)

COMM 289C: Projects for Publication

In-depth journalism projects are not products of happenstance. They require thorough planning and coordination at every stage of the process -- from refinement of ideas, to the creation of "back-out" schedules and precise outlines, to strategies for pitching the story and its author to skeptical editors. In this course, students will workshop and pitch MA journalism projects for placement and publication. Required for MA Journalism students; registration Comm 289 required.
Terms: Spr | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Brenner, R. (PI)

COMM 290: Media Studies M.A. Project

Individual research for coterminal Media Studies students.
Terms: Aut, Win, Spr, Sum | Units: 1-2 | Repeatable for credit | Grading: Satisfactory/No Credit

COMM 291: Graduate Journalism Seminar

Required of students in the graduate program in Journalism. Forum for current issues in the practice and performance of the press. The seminar frequently features Bay Area Journalists as guest speakers. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

COMM 299: Individual Work

Terms: Aut, Win, Spr, Sum | Units: 1-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

COMM 301: Communication Research, Curriculum Development and Pedagogy

Designed to prepare students for teaching and research in the Department of Communication. Students will be trained in developing curriculum and in pedagogical practices, and will also be exposed to the research programs of various faculty members in the department. Required of all Ph.D. students.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Bailenson, J. (PI)

COMM 307: Summer Institute in Political Psychology

Lectures, discussion groups, and workshops addressing many applications of psychology to the analysis of political behavior. Public opinion, international relations, political decision-making, attitudes and beliefs, prejudice, social influence and persuasion, terrorism, news media influence, foreign policy, socialization, social justice.
Terms: Sum | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Krosnick, J. (PI)

COMM 308: Graduate Seminar in Political Psychology (POLISCI 324)

For students interested in research in political science, psychology, or communication. Methodological techniques for studying political attitudes and behaviors. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

COMM 310: Method of Analysis Program in the Social Sciences (ANTHRO 446A)

Colloquium series. Creation and application of new methodological techniques for social science research. Presentations on methodologies of use for social scientists across departments at Stanford by guest speakers from Stanford and elsewhere. See http://mapss.stanford.edu.
Terms: not given this year | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

COMM 311: Theory of Communication

Basic communication theory for first-year Ph.D. students in the Department of Communication. Introduction to basic writings and concepts in communication research. The goal is an introduction to issues in the field that are common in communication research. First half of the class will emphasize classic literature about field organization, history and theory. Second half will emphasize contemporary theory in areas that students select.
Terms: Aut | Units: 1-5 | Grading: Letter (ABCD/NP)
Instructors: Reeves, B. (PI)

COMM 312: Models of Democracy (COMM 212, POLISCI 237, POLISCI 337)

Ancient and modern varieties of democracy; debates about their normative and practical strengths and the pathologies to which each is subject. Focus is on participation, deliberation, representation, and elite competition, as values and political processes. Formal institutions, political rhetoric, technological change, and philosophical critique. Models tested by reference to long-term historical natural experiments such as Athens and Rome, recent large-scale political experiments such as the British Columbia Citizens' Assembly, and controlled experiments.
Terms: not given this year | Units: 3-5 | Grading: Letter or Credit/No Credit

COMM 314: Qualitative Social Science Research Methods

Part of the doctoral research methods sequence. Focus is on the logic of qualitative research methods and modes of inquiry relevant to the study of communication and meaning. Prerequisite: Communication Ph.D. student, or consent of instructor.
Terms: Win | Units: 1-5 | Grading: Letter (ABCD/NP)
Instructors: Glasser, T. (PI)

COMM 317: The Philosophy of Social Science

Approaches to social science research and their theoretical presuppositions. Readings from the philosophy of the social sciences. Research design, the role of experiments, and quantitative and qualitative research. Cases from communication and related social sciences. Prerequisite: consent of instructor.
Terms: given next year | Units: 1-5 | Grading: Letter (ABCD/NP)

COMM 318: Quantitative Social Science Research Methods

An introduction to a broad range of social science research methods that are widely used in PhD work. Prerequisite: consent of instructor.
Terms: Win | Units: 1-5 | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

COMM 320G: Advanced Topics in New Media and American Culture

This course deals with advanced issues in computing and American cultural history since World War II. Primarily for Ph.D. students. Prerequisite: 220 or consent of instructor.
Terms: Win | Units: 1-5 | Grading: Letter (ABCD/NP)
Instructors: Turner, F. (PI)

COMM 326: Advanced Topics in Human Virtual Representation

Topics include the theoretical construct of person identity, the evolution of that construct given the advent of virtual environments, and methodological approaches to understanding virtual human representation. Prerequisite: PhD student or consent of instructor.
Terms: Spr | Units: 1-5 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Bailenson, J. (PI)

COMM 331G: Communication and Media Ethics

Limited to Ph.D. students. Advanced topics in press ethics and responsibility. Prerequisite: 231 or consent of instructor.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit
Instructors: Glasser, T. (PI)

COMM 335: Deliberative Democracy and its Critics (AMSTUD 135, COMM 135, COMM 235, POLISCI 234P, POLISCI 334P)

This course examines the theory and practice of deliberative democracy and engages both in a dialogue with critics. Can a democracy which emphasizes people thinking and talking together on the basis of good information be made practical in the modern age? What kinds of distortions arise when people try to discuss politics or policy together? The course draws on ideas of deliberation from Madison and Mill to Rawls and Habermas as well as criticisms from the jury literature, from the psychology of group processes and from the most recent normative and empirical literature on deliberative forums. Deliberative Polling, its applications, defenders and critics, both normative and empirical, will provide a key case for discussion.
Terms: Spr | Units: 3-5 | Grading: Letter or Credit/No Credit
Instructors: Fishkin, J. (PI); Siu, A. (PI)

COMM 339: Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives (POLISCI 421K, PSYCH 231)

The social and psychological processes involved in asking and answering questions via questionnaires for the social sciences; optimizing questionnaire design; open versus closed questions; rating versus ranking; rating scale length and point labeling; acquiescence response bias; don't-know response options; response choice order effects; question order effects; social desirability response bias; attitude and behavior recall; and introspective accounts of the causes of thoughts and actions.
Terms: Spr | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

COMM 368: Experimental Research in Advanced User Interfaces (COMM 168, COMM 268, ME 468)

Project-based course involves small (3-4) person teams going through all parts of the experimental process: question generation, experiment design, running, and data analysis. Each team creates an original, publishable project that represents a contribution to the research and practicum literatures. All experiments involve interaction between people and technology, including cars, mobile phones, websites, etc. Prerequisite: consent of instructor.
Terms: Aut, Win | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Nass, C. (PI)

COMM 372G: Seminar in Psychological Processing

Limited to Ph.D. students. Advanced topics. Prerequisite: 272 or consent of instructor.
Terms: Win | Units: 1-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Reeves, B. (PI)

COMM 379: History of the Study of Communication

The origins of communication/media theory and research emphasizing the rise of communication as a separate field of study. The influence of schools of thought concerning the scope and purpose of the study of communication. Readings include foundational essays and studies. Prerequisite: Ph.D. student or consent of instructor.
Terms: not given this year | Units: 1-5 | Grading: Letter (ABCD/NP)

COMM 380: Curriculum Practical Training

Practical experience in the communication industries. Prerequisites: graduate standing in Communication, consent of instructor. Meets requirements for Curricular Practical Training for students on F-1 visas. 380 May be repeated four times for credit. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit | Grading: Letter or Credit/No Credit

COMM 397: Minor Research Project

Individual research for Ph.D. candidates. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Satisfactory/No Credit

COMM 398: Major Research Project

Individual research for Ph.D. candidates.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Satisfactory/No Credit

COMM 399: Advanced Individual Work

Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit | Grading: Letter (ABCD/NP)

COMM 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

COMM 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

EE 15N: The Art and Science of Engineering Design

The goal of this seminar is to introduce freshmen to the design process associated with an engineering project. The seminar will consist of a series of lectures. The first part of each lecture will focus on the different design aspects of an engineering project, including formation of the design team, developing a project statement, generating design ideas and specifications, finalizing the design, and reporting the outcome. Students will form teams to follow these procedures in designing a term project of their choice over the quarter. The second part of each lecture will consist of outside speakers, including founders of some of the most exciting companies in Silicon Valley, who will share their experiences about engineering design. On-site visits to Silicon Valley companies to showcase their design processes will also be part of the course. The seminar serves three purposes: (1) it introduces students to the design process of turning an idea into a final design, (2) it presents the different functions that people play in a project, and (3) it gives students a chance to consider what role in a project would be best suited to their interests and skills.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Goldsmith, A. (PI); Le, M. (PI)

EE 17N: Engineering the Micro and Nano Worlds: From Chips to Genes

Preference to freshmen. The first part is hands-on micro- and nano-fabrication including the Stanford Nanofabrication Facility (SNF) and the Stanford Nanocharacterization Laboratory (SNL) and field trips to local companies and other research centers to illustrate the many applications; these include semiconductor integrated circuits ('chips'), DNA microarrays, microfluidic bio-sensors and microelectromechanical systems (MEMS). The second part is to create, design, propose and execute a project. Most of the grade will be based on the project. By the end of the course you will, of course, be able to read critically a New York Times article on nanotechnology. More importantly you will have experienced the challenge (and fun) of designing, carrying out and presenting your own experimental project. As a result you will be better equipped to choose your major. This course can complement (and differs from) the seminars offered by Profs Philip Wong and Hari Manoharan in that it emphasizes laboratory work and an experimental student-designed project. Prerequisites: high-school physics.
Terms: Spr | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Pease, R. (PI)

EE 21N: What is Nanotechnology?

Nanotechnology is an often used word and it means many things to different people. Scientists and Engineers have some notion of what nanotechnology is, societal perception may be entirely different. In this course, we start with the classic paper by Richard Feynman ("There's Plenty of Room at the Bottom"), which laid down the challenge to the nanotechnologists. Then we discuss two classic books that offer a glimpse of what nanotechnology is: Engines of Creation: The Coming Era of Nanotechnology by Eric Drexler, and Prey by Michael Crichton. Drexler's thesis sparked the imagination of what nano machinery might do, whereas Crichton's popular novel channeled the public's attention to this subject by portraying a disastrous scenario of a technology gone astray. We will use the scientific knowledge to analyze the assumptions and predictions of these classic works. We will draw upon the latest research advances to illustrate the possibilities and impossibilities of nanotechnology.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Wong, P. (PI)

EE 23N: Imaging: From the Atom to the Universe

Preference to freshmen. Forms of imaging including human and animal vision systems, atomic force microscope, microscope, digital camera, holography and three-dimensional imaging, telescope, synthetic aperture radar imaging, nuclear magnetic imaging, sonar and gravitational wave imaging, and the Hubble Space telescope. Physical principles and exposure to real imaging devices and systems.
Terms: Spr | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Hesselink, L. (PI)

EE 27N: Electronics Rocks

Electronics pervades our lives, yet we often feel obliged to let a device function as it was intended. This course is about not being intimidated by voiding a warranty and modding some commercial gadget and about being confident enough to build something cool from scratch. To get there, we will study the basics of "how things work" and learn how to hack/mod and scratch build. Students will be mentored and encouraged to work, in teams, to play with interesting electronics and ultimately to develop a creative final project.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Kovacs, G. (PI)

EE 41: Physics of Electrical Engineering (ENGR 40P)

How everything from electrostatics to quantum mechanics is used in common high-technology products. Electrostatics are critical in micro-mechanical systems used in many sensors and displays, and Electromagnetic waves are essential in all high-speed communication systems. How to propagate energy on transmission lines, optical fibers,and in free space. Which aspects of modern physics are needed to generate light for the operation of a DVD player or TV. Introduction to semiconductors, solid-state light bulbs, and laser pointers. Hands-on labs to connect physics to everyday experience. Prerequisites: Physics 43
Terms: Win | Units: 5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Solgaard, O. (PI)

EE 46: Engineering For Good: Save the World and Have Fun Doing It

Projects that provide immediate and positive impact on the world. Focus is on global health by learning from experts in this field. Students work on real-world projects with help from members of NGOs and social entrepreneurial companies as part of the hand-on learning experience. Prerequisite: ENGR 40 or EE 122A or CS 106B or consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Le, M. (PI)

EE 47: Press Play: Interactive Device Design

Introduction to the human-centered and technical workings behind interactive devices ranging from cellphones and video controllers to smart cars and appliances. Students build a working MP3 player prototype of their own design, using embedded microcontrollers, digital audio decoders and component sensors, and other electronic hardware. Topics include electronics prototyping, interface prototyping, sensors and actuators, micro-controller development, physical prototyping, and user testing. Prerequisite: CS106A and X or consent of instructor.
Terms: Spr, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ju, W. (PI)

EE 60N: Man versus Nature: Coping with Disasters Using Space Technology (GEOPHYS 60N)

Preference to freshman. Natural hazards, earthquakes, volcanoes, floods, hurricanes, and fires, and how they affect people and society; great disasters such as asteroid impacts that periodically obliterate many species of life. Scientific issues, political and social consequences, costs of disaster mitigation, and how scientific knowledge affects policy. How spaceborne imaging technology makes it possible to respond quickly and mitigate consequences; how it is applied to natural disasters; and remote sensing data manipulation and analysis. GER:DB-EngrAppSci
Terms: Aut | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Zebker, H. (PI)

EE 92A: Making and Breaking Things

This course will feature weekly visiting speakers who will guide class members through the hands-on process of assembling or dissection novel interactive devices and products. The course is meant to provide students hands-on experience with component sensing and computing technolo-gies, a working knowledge of different materials and methods used in modern-day prototyping and manufacture, and exposure to people en-gaged in designing novel devices within the field of interactive device de-sign. Activities will features a wide and evolving range of domains such as texile sensors, hacking wireless radio, making LED light sculptures, taking apart toys, shape deposition modeling and more.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

EE 100: The Electrical Engineering Profession

Lectures/discussions on topics of importance to the electrical engineering professional. Continuing education, professional societies, intellectual property and patents, ethics, entrepreneurial engineering, and engineering management.
Terms: Aut | Units: 1-2 | Grading: Satisfactory/No Credit
Instructors: Dutton, R. (PI)

EE 101A: Circuits I

First of two-course sequence. Introduction to circuit modeling and analysis. Topics include creating the models of typical components in electronic circuits and simplifying non-linear models for restricted ranges of operation (small signal model); and using network theory to solve linear and non-linear circuits under static and dynamic operations. Prerequisite: Physics 43
Terms: Win | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Wong, S. (PI)

EE 101B: Circuits II

Second of two-course sequence. MOS large-signal and small-signal models. MOS amplifier design including DC bias, small signal performance, multistage amplifiers, frequency response, and feedback. Prerequisite: 101A.
Terms: Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Arbabian, M. (PI)

EE 102A: Signal Processing and Linear Systems I

Concepts and mathematical tools in continuous-time signal processing and linear systems analysis, illustrated with examples from signal processing, communications, and control. Mathematical representation of signals and systems. Linearity and time-invariance. System impulse and step response. Frequency domain representations: Fourier series and Fourier transforms. Filtering and signal distortion. Time/frequency sampling and interpolation. Continuous-discrete time signal conversion and quantization.Stability and causality in linear systems. Laplace transforms and Bode plots. Feedback and control system design. Examples from filter design and linear control. Prerequisite: MATH 53 or ENGR 155A.
Terms: Win, Sum | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Pauly, J. (PI)

EE 102B: Signal Processing and Linear Systems II

Concepts and mathematical tools in discrete-time signal processing and linear systems analysis with examples from digital signal processing, communications, and control. Discrete-time signal models. Continuous-discrete-continuous signal conversion. Discrete-time impulse and step response. Frequency domain representations: Fourier series and transforms. Connection between continuous and discrete time frequency representations. Discrete Fourier transform (DFT) and fast Fourier transform (FFT). Digital filter and signal processing examples. Discrete-time and hybrid linear systems. Stability and causality. Z transforms and their connection to Laplace transforms. Frequency response of discrete-time systems. Discrete-time control. Prerequisite: 102A.
Terms: Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Schafer, R. (PI)

EE 108A: Digital Systems I

Digital circuit, logic, and system design. Digital representation of information. CMOS logic circuits. Combinational logic design. Logic building blocks, idioms, and structured design. Sequential logic design and timing analysis. Clocks and synchronization. Finite state machines. Microcode control. Digital system design. Control and datapath partitioning. Undergraduates must enroll for 4 units.
Terms: Aut, Win | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Mitra, S. (PI)

EE 108B: Digital Systems II

The design of processor-based digital systems. Instruction sets, addressing modes, data types. Assembly language programming, low-level data structures, introduction to operating systems and compilers. Processor microarchitecture, microprogramming, pipelining. Memory systems and caches. Input/output, interrupts, buses and DMA. System design implementation alternatives, software/hardware tradeoffs. Labs involve the design of processor subsystems and processor-based embedded systems. Prerequisite: 108A, CS 106B.
Terms: Aut, Win | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

EE 109: Digital Systems Design Lab

The design of integrated digital systems encompassing both customized software and hardware. Software/hardware design tradeoffs. Algorithm design for pipelining and parallelism. System latency and throughput tradeoffs. FPGA optimization techniques. Integration with external systems and smart devices. Firmware configuration and embedded system considerations. Enrollment limited to 25; preference to graduating seniors. Prerequisites: 108B, and CS 106B or X.
Terms: Spr | Units: 4 | Grading: Letter or Credit/No Credit
Instructors: Olukotun, O. (PI)

EE 114: Fundamentals of Analog Integrated Circuit Design (EE 214A)

Analysis and simulation of elementary transistor stages, current mirrors, supply- and temperature-independent bias, and reference circuits. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. Performance evaluation using computer-aided design tools. Prerequisite: 101B.GER:DB-EngrAppSci
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Dutton, R. (PI)

EE 116: Semiconductor Device Physics

The fundamental operation of semiconductor devices and overview of applications. The physical principles of semiconductors, both silicon and compound materials; operating principles and device equations for junction devices (diodes, bipolar transistor, photo-detectors). Introduction to quantum effects and band theory of solids. Prerequisite: ENGR 40. Corequisite: 101B.
Terms: Spr | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Staff, 1. (PI)

EE 122A: Analog Circuits Laboratory

Practical applications of analog circuits, including simple amplifiers, filters, oscillators, power supplies, and sensors. Design skills, computer-aided design, and circuit fabrication and debugging. The design process through proposing, designing, simulating, building, debugging, and demonstrating a project. Radio frequency and largely digital projects not suitable for EE 122. Prerequisite: ENGR 40 or equivalent.
Terms: Aut | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Kovacs, G. (PI)

EE 122B: Introduction to Biomedical Electronics

Key components of modern systems, their application in physiology measurements, and reduction to practice in labs. Fundamentals of analog/digital conversion and filtering techniques for biosignals, typical transducers (biopotential, electrochemical, temperature, pressure, acoustic, movement), and interfacing circuits. Issues of biomedical electronics (safety, noise). Prerequisite: EE122A or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kovacs, G. (PI)

EE 124: Introduction to Neuroelectrical Engineering

Fundamental properties of electrical activity in neurons, technology for measuring and altering neural activity, and operating principles of modern neurological and neural prosthetic medical systems. Topics: action potential generation and propagation, neuro-MEMS and measurement systems, experimental design and statistical data analysis, information encoding and decoding, clinical diagnostic systems, and fully-implantable neural prosthetic systems design. Prerequisite: EE 101B and EE 102B.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 133: Analog Communications Design Laboratory (EE 233)

Design, testing, and applications. Amplitude modulation (AM) using multiplier circuits. Frequency modulation (FM) based on discrete oscillator and integrated modulator circuits such as voltage-controlled oscillators (VCOs). Phased-lock loop (PLL) techniques, characterization of key parameters, and their applications. Practical aspects of circuit implementations. Labs involve building and characterization of AM and FM modulation/demodulation circuits and subsystems. Enrollment limited to 30 undergraduates and coterminal EE students. Prerequisite: EE101B. Undergraduate students enroll in EE133 and Graduate students enroll in EE233. Recommended: EE114/214A.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Dutton, R. (PI)

EE 134: Introduction to Photonics

Photonics, optical components, and fiber optics. Conceptual and mathematical tools for design and analysis of optical communication, sensor and imaging systems. Experimental characterization of semiconductor lasers, optical fibers, photodetectors, receiver circuitry, fiber optic links, optical amplifiers, and optical sensors. Class project on confocal microscopy or other method of sensing or analyzing biometric data. Laboratory experiments. Prerequisite: 41 or equivalent.
Terms: Spr | Units: 4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Ellerbee, A. (PI)

EE 136: Introduction to Nanophotonics and Nanostructures

Electromagnetic and quantum mechanical waves and semiconductors. Confining these waves, and devices employing such confinement. Localization of light and applications: metallic mirrors, photonic crystals, optical waveguides, microresonators, plasmonics. Localization of quantum mechanical waves: quantum wells, wires, and dots. Generation of light in semiconductors: spontaneous and stimulated emission, lasers, and light emitting diodes. Devices incorporating localization of both electromagnetic and quantum mechanical waves such as resonant cavity quantum well lasers and microcavity-based single photon sources. System-level applications such as optical communications, biochemical sensing, and quantum cryptography. Prerequisite: basic familiarity with electromagnetic and quantum mechanical waves and semiconductors at the level of EE 41 or equivalent.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Vuckovic, J. (PI)

EE 141: Engineering Electromagnetics

Lumped versus distributed circuits. Transient response of transmission lines with resistive and reactive loads. Reflection, transmission, attenuation and dispersion. Steady-state waves on transmission lines. Standing wave ratio, impedance matching, and power flow. Coulomb's law, electrostatic field, potential and gradient, electric flux and Gauss's Law and divergence. Metallic conductors, Poisson's and Laplace's equations, capacitance, dielectric materials. Electrostatic energy and forces. Steady electric currents, Ohm's Law, Kirchoff's Laws, charge conservation and the continuity equation, Joule's Law. Biot-Savart's law and the static magnetic field. Ampere's Law and curl. Vector magnetic potential and magnetic dipole. Magnetic materials, forces and torques. Faraday's Law, magnetic energy, displacement current and Maxwell's equations. Uniform plane waves. Prerequisites: 102A, MATH 52.
Terms: Win | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter (ABCD/NP)
Instructors: Hesselink, L. (PI)

EE 152: Green Electronics

Many ¿green technologies¿ including hybrid cars, photovoltaic energy systems, efficient power supplies, and energy-conserving control systems have at their heart intelligent, high-power electronics. This course examines this technology and uses green-tech examples to teach the engineering principles of modeling, optimization, analysis, simulation, and design. Topics include power converter topologies, periodic steady-state analysis, control, motors and drives, photovol-taic systems, and design of magnetic components. The course involves a hands-on laboratory and a substantial final project. Required: EE101B, EE102A, EE108A. Recommended: ENGR40 or EE122A.
Terms: Aut | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Dally, W. (PI)

EE 168: Introduction to Digital Image Processing

Computer processing of digital 2-D and 3-D data, combining theoretical material with implementation of computer algorithms. Topics: properties of digital images, design of display systems and algorithms, time and frequency representations, filters, image formation and enhancement, imaging systems, perspective, morphing, and animation applications. Instructional computer lab exercises implement practical algorithms. Final project consists of computer animations incorporating techniques learned in class. Prerequisite: Matlab programming.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

EE 169: Introduction to Bioimaging

Bioimaging is important for both clinical medicine, and medical research. This course will provide a introduction to several of the major imaging modalities, using a signal processing perspective. The course will start with an introduction to multi-dimensional Fourier transforms, and image quality metrics. It will then study projection imaging systems (projection X-Ray), backprojection based systems (CT, PET, and SPECT), systems that use beam forming (ultrasound), and systems that use Fourier encoding (MRI). Prerequisites: 102A, 102B
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pauly, J. (PI)

EE 178: Probabilistic Systems Analysis (EE 278A)

Introduction to probability and statistics and their role in modeling and analyzing real world phenomena. Events, sample space, and probability. Discrete random variables, probability mass functions, independence and conditional probability, expectation and conditional expectation. Continuous random variables, probability density functions, independence and expectation, derived densities. Transforms, moments, sums of independent random variables. Simple random processes. Limit theorems. Introduction to statistics: significance, estimation and detection. Prerequisites: basic calculus and linear algebra.
Terms: Aut, Spr | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Montanari, A. (PI); Ozgur Aydin, A. (PI)

EE 179: Analog and Digital Communication Systems

This course covers the fundamental principles underlying the analysis, design and optimization of analog and digital communication systems. Design examples will be taken from the most prevalent communication systems today: cell phones, Wifi, radio and TV broadcasting, satellites, and computer networks. Analysis techniques based on Fourier transforms and energy/power spectral density will be developed. Mathematical models for random variables and random (noise) signals will be presented, which are used to characterize filtering and modulation of random noise. These techniques will then be used to design analog (AM and FM) and digital (PSK and FSK) communication systems and determine their performance over channels with noise and interference. Prerequisite: 102A.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Gill, J. (PI)

EE 190: Special Studies or Projects in Electrical Engineering

Independent work under the direction of a faculty member. Individual or team activities involve lab experimentation, design of devices or systems, or directed reading. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Bahai, A. (PI); Baker, M. (PI); Bambos, N. (PI); Beasley, M. (PI); Binford, T. (PI); Boahen, K. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Chidsey, C. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); DaRosa, A. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Ellerbee, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Eshleman, V. (PI); Fan, S. (PI); Flynn, M. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Giovangrandi, L. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Heeger, D. (PI); Helliwell, R. (PI); Hellman, M. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Kailath, T. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kiehl, R. (PI); Kim, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lee, T. (PI); Leeson, D. (PI); Levin, C. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Luckham, D. (PI); Macovski, A. (PI); Manoharan, H. (PI); Marcus, B. (PI); McCluskey, E. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Popelka, G. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Zebker, H. (PI)

EE 191: Special Studies and Reports in Electrical Engineering

Independent work under the direction of a faculty member given for a letter grade only. If a letter grade given on the basis of required written report or examination is not appropriate, enroll in 190. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Bahai, A. (PI); Baker, M. (PI); Bambos, N. (PI); Beasley, M. (PI); Binford, T. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Carpenter, D. (PI); Cheriton, D. (PI); Chidsey, C. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); DaRosa, A. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Ellerbee, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Eshleman, V. (PI); Fan, S. (PI); Flynn, M. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Giovangrandi, L. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Heeger, D. (PI); Helliwell, R. (PI); Hellman, M. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Huang, K. (PI); Inan, U. (PI); Kahn, J. (PI); Kailath, T. (PI); Katti, S. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kiehl, R. (PI); Kim, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lauben, D. (PI); Lee, T. (PI); Leeson, D. (PI); Levin, C. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Luckham, D. (PI); Macovski, A. (PI); Manoharan, H. (PI); Marcus, B. (PI); McCluskey, E. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Moslehi, M. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Popelka, G. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Zebker, H. (PI)

EE 191A: Special Studies and Reports in Electrical Engineering

EE191A is part of the Accelerated Calculus for Engineers program. Independent work under the direction of a faculty member given for a letter grade only. EE 191A counts as a Math one unit seminar course: it is this unit that constitutes the ACE program.
Terms: Aut, Win, Spr | Units: 1 | Grading: Letter (ABCD/NP)

EE 191W: Special Studies and Reports in Electrical Engineering (WIM)

WIM-version of EE 191. For EE students using special studies (e.g., honors project, independent research project) to satisfy the writing-in-major requirement. A written report that has gone through revision with an advisor is required. An advisor from the Writing Center is recommended.
Terms: Aut, Win, Spr, Sum | Units: 3-10 | Grading: Letter (ABCD/NP)

EE 202: Electrical Engineering in Biology and Medicine

Open to all. Primarily biological in nature, introduction to the physiological and anatomic aspects of medical instrumentation. Areas include patient monitoring, imaging, medical transducers, the unique aspects of medical electronic systems, the socio-economic impact of technology on medical care, and the constraints unique to medicine. Prerequisite: familiarity with circuit instrumentation techniques as in 101B.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Poon, A. (PI)

EE 203: The Entrepreneurial Engineer

Seminar. For prospective entrepreneurs with an engineering background. Contributions made to the business world by engineering graduates. Speakers include Stanford and other engineering and M.B.A. graduates who have founded large and small companies in nearby communities. Contributions from EE faculty and other departments including Law, Business, and MS&E.May be repeated for credit.
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Melen, R. (PI)

EE 204: Business Management for Electrical Engineers and Computer Scientists

For graduate students with little or no business experience. Leading computer, high-tech, and Silicon Valley companies and their best practices. Tools and frameworks for analyzing decisions these companies face. Corporate strategy, new product development, marketing, sales, distribution, customer service, financial accounting, outsourcing, and human behavior in business organizations. Case studies. Prerequisite: graduate standing.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

EE 204S: Business Management for Electrical Engineers and Computer Scientists

For SCPD students; see EE204.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Gibbons, F. (PI)

EE 212: Integrated Circuit Fabrication Processes

For students interested in the physical bases and practical methods of silicon VLSI chip fabrication, or the impact of technology on device and circuit design, or intending to pursue doctoral research involving the use of Stanford's Nanofabrication laboratory. Process simulators illustrate concepts. Topics: principles of integrated circuit fabrication processes, physical and chemical models for crystal growth, oxidation, ion implantation, etching, deposition, lithography, and back-end processing. Required for 410.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Plummer, J. (PI)

EE 214A: Fundamentals of Analog Integrated Circuit Design (EE 114)

Analysis and simulation of elementary transistor stages, current mirrors, supply- and temperature-independent bias, and reference circuits. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. Performance evaluation using computer-aided design tools. Prerequisite: 101B.GER:DB-EngrAppSci
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Dutton, R. (PI)

EE 214B: Advanced Analog Integrated Circuit Design

Analysis and design of analog integrated circuits in advanced MOS and bipolar technologies. Device operation and compact modeling in support of circuit simulations needed for design. Emphasis on quantitative evaluations of performance using hand calculations and circuit simulations; intuitive approaches to design. Analytical and approximate treatments of noise and distortion; analysis and design of feedback circuits. Design of archetypal analog blocks for networking and communications such as broadband gain stages and transimpedance amplifiers. Prerequisites: EE114/214A.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Murmann, B. (PI)

EE 216: Principles and Models of Semiconductor Devices

Carrier generation, transport, recombination, and storage in semiconductors. Physical principles of operation of the p-n junction, heterojunction, metal semiconductor contact, bipolar junction transistor, MOS capacitor, MOS and junction field-effect transistors, and related optoelectronic devices such as CCDs, solar cells, LEDs, and detectors. First-order device models that reflect physical principles and are useful for integrated-circuit analysis and design. Prerequisite: 116 or equivalent.
Terms: Aut, Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Harris, J. (PI); Nainani, A. (PI); Saraswat, K. (PI)

EE 222: Applied Quantum Mechanics I

Emphasis is on applications in modern devices and systems. Topics include: Schrödinger's equation, eigenfunctions and eigenvalues, solutions of simple problems including quantum wells and tunneling, quantum harmonic oscillator, coherent states, operator approach to quantum mechanics, Dirac notation, angular momentum, hydrogen atom, calculation techniques including matrix diagonalization, perturbation theory, variational method, and time-dependent perturbation theory with applications to optical absorption, nonlinear optical coefficients, and Fermi's golden rule. Prerequisites: MATH 52 and 53, PHYSICS 65 (or PHYSICS 43 and 45).
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Miller, D. (PI)

EE 223: Applied Quantum Mechanics II

Continuation of 222, including more advanced topics: quantum mechanics of crystalline materials, methods for one-dimensional problems, spin, systems of identical particles (bosons and fermions), introductory quantum optics (electromagnetic field quantization, coherent states), fermion annihilation and creation operators, interaction of different kinds of particles (spontaneous emission, optical absorption, and stimulated emission). Quantum information and interpretation of quantum mechanics. Other topics in electronics, optoelectronics, optics, and quantum information science. Prerequisite: 222.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Miller, D. (PI)

EE 225: Bio-chips, Imaging and Nanomedicine (MATSCI 382, SBIO 225)

The course covers state-of-the-art and emerging bio-sensors, bio-chips, imaging modalities, and nano-therapies which will be studied in the context of human physiology including the nervous system, circulatory system and immune system. Medical diagnostics will be divided into bio-chips (in-vitro diagnostics) and medical and molecular imaging (in-vivo imaging). In-depth discussion on cancer and cardiovascular diseases and the role of diagnostics and nano-therapies.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

EE 228: Basic Physics for Solid State Electronics

Topics: energy band theory of solids, energy bandgap engineering, classical kinetic theory, statistical mechanics, and equilibrium and non-equilibrium semiconductor statistics. Prerequisite: course in modern physics.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fan, S. (PI)

EE 231: Introduction to Lasers

How lasers work, including quantum transitions in atoms, stimulated emission and amplification, rate equations, saturation, feedback, coherent optical oscillation, laser resonators, and optical beams. Limited primarily to steady-state behavior; classical models for atomic transitions with little quantum mechanics background required. Prerequisites: electromagnetic theory to the level of 142, preferably 241, and some atomic or modern physics such as PHYSICS 70 or 130, 131.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kim, N. (PI)

EE 232: Laser Dynamics

Continuation of 231, emphasizing dynamic and transient effects including spiking, Q-switching, mode locking, frequency modulation, frequency and spatial mode competition, linear and nonlinear pulse propagation, short pulse expansion, and compression. Prerequisite: 231.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fejer, M. (PI)

EE 233: Analog Communications Design Laboratory (EE 133)

Design, testing, and applications. Amplitude modulation (AM) using multiplier circuits. Frequency modulation (FM) based on discrete oscillator and integrated modulator circuits such as voltage-controlled oscillators (VCOs). Phased-lock loop (PLL) techniques, characterization of key parameters, and their applications. Practical aspects of circuit implementations. Labs involve building and characterization of AM and FM modulation/demodulation circuits and subsystems. Enrollment limited to 30 undergraduates and coterminal EE students. Prerequisite: EE101B. Undergraduate students enroll in EE133 and Graduate students enroll in EE233. Recommended: EE114/214A.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Dutton, R. (PI)

EE 234: Photonics Laboratory

Photonics and fiber optics with a focus on communication and sensing. Experimental characterization of semiconductor lasers, optical fibers, photodetectors, receiver circuitry, fiber optic links, optical amplifiers, and optical sensors and photonic crystals. Prerequisite: EE 142 or equivalent.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Solgaard, O. (PI)

EE 235: Guided Wave Optical Devices

Guided wave optics, optical waveguide devices, and integrated optics. Wave propagation in layered media, slab waveguides, and optical fibers. Rectangular waveguides. Optical waveguide technology. Coupled-mode theory. Numerical analysis of complex waveguides. Photonic crystals and surface plasmon optics. Physics and design of waveguide devices. Fiber sensors, waveguide gratings, waveguide modulators, directional couplers, ring filters. Prerequisite: electromagnetic theory to the level of 142 or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 242: Electromagnetic Waves

Continuation of 141. Maxwell's equations. Plane waves in lossless and lossy media. Skin effect. Flow of electromagnetic power (Poynting's theorem). Reflection and refraction of waves at planar boundaries. Snell's law and total internal reflection. Reflection and refraction from lossy media. Guided waves. Parallel-plate and dielectric-slab waveguides. Hollow wave-guides, cavity resonators, microstrip waveguides, optical fibers. Interaction of fields with matter and particles. Antennas and radiation of electromagnetic energy. Prerequisite: 141 or PHYSICS 120.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Fraser-Smith, A. (PI)

EE 243: Semiconductor Optoelectronic Devices

Semiconductor physics and optical processes in semiconductors. Operating principles and practical device features of semiconductor optoelectronic materials and heterostructures. Devices include: optical detectors (p-i-n, avalanche, and MSM); light emitting diodes; electroabsorptive modulators (Franz-Keldysh and QCSE), electrorefractive (directional couplers, Mach-Zehnder), switches (SEEDs); and lasers (waveguide and vertical cavity surface emitting). Prerequisites: semiconductor devices and solid state physics such as EE 216 or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Harris, J. (PI)

EE 247: Introduction to Optical Fiber Communications

Fibers: single- and multi-mode, attenuation, modal dispersion, group-velocity dispersion, polarization-mode dispersion. Nonlinear effects in fibers: Raman, Brillouin, Kerr. Self- and cross-phase modulation, four-wave mixing. Sources: light-emitting diodes, laser diodes, transverse and longitudinal mode control, modulation, chirp, linewidth, intensity noise. Modulators: electro-optic, electro-absorption. Photodiodes: p-i-n, avalanche, responsivity, capacitance, transit time. Receivers: high-impedance, transimpedance, bandwidth, noise. Digital intensity modulation formats: non-return-to-zero, return-to-zero. Receiver performance: Q factor, bit-error ratio, sensitivity, quantum limit. Sensitivity degradations: extinction ratio, intensity noise, jitter, dispersion. Wavelength-division multiplexing. System architectures: local-area, access, metropolitan-area, long-haul. Prerequisites: 102A, 242 or consent of instructor.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kahn, J. (PI)

EE 248: Fundamentals of Noise Processes

Fundamentals of statistic, Fourier analysis, statistical and quantum mechanics, and linear and nonlinear circuit theory. Thermal, quantum and 1/f noise in resistors, pn junctions, lasers, and parametric amplifiers. Energy efficiency (bit/photon) and spectral efficiency (bit/s/Hz) in coherent and single photon optical communications. Protocols and security in quantum cryptography. Decoherence of qubits in quantum computation. Prerequisites: elementary device, circuit, and electromagnetic waves to the level of 101A,B and 242.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 252: Antenna Theory

This course aims to cover the theory, simulation, and hands-on experiment in antenna design. Topics include: basic parameters to describe the performance and characteristics of an antenna, link budget analyses, solving the fields from a Hertizian dipole, duality, equivalence principle, reciprocity, linear wire antenna, circular loop antenna, antenna array, slot and patch antennas, helical antennas, wideband antennas, size reduction techniques, wideband small antennas, and circularly polarized (CP) small antennas. Prerequisite: EE 141 or Physics 120. Enrollment capacity limited to 25 students.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Poon, A. (PI)

EE 256: Numerical Electromagnetics

Principles and applications of numerical techniques for solving practical electromagnetics problems. Time domain solutions of Maxwell's equations. Finite difference time domain (FDTD) methods. Numerical stability, dispersion, and dissipation. Absorbing boundary conditions. Perfectly matched layer methods. Explicit and implicit methods. FDTD modeling of propagation and scattering in dispersive and anisotropic media. Near-to-far-zone transformations. Computational problems require programming and use of MATLAB and other tools. Prerequisite: EE 242 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258)

Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of large-scale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended: EE261, EE263, EE178/278A, ME300 or equivalent.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Zebker, H. (PI)

EE 261: The Fourier Transform and Its Applications

The Fourier transform as a tool for solving physical problems. Fourier series, the Fourier transform of continuous and discrete signals and its properties. The Dirac delta, distributions, and generalized transforms. Convolutions and correlations and applications; probability distributions, sampling theory, filters, and analysis of linear systems. The discrete Fourier transform and the FFT algorithm. Multidimensional Fourier transform and use in imaging. Further applications to optics, crystallography. Emphasis is on relating the theoretical principles to solving practical engineering and science problems. Prerequisites: Math through ODEs, basic linear algebra, Comfort with sums and discrete signals, Fourier series at the level of 102A
Terms: Aut, Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit

EE 262: Two-Dimensional Imaging

Time and frequency representations, two-dimensional auto- and cross-correlation, Fourier spectra, diffraction and antennas, coordinate systems and the Hankel and Abel transforms, line integrals, impulses and sampling, restoration in the presence of noise, reconstruction and tomography, imaging radar. Tomographic reconstruction using projection-slice and layergarm methods. Students create software to form images using these techniques with actual data. Final project consists of design and simulation of an advanced imaging system. Prerequisite: EE261. Recommended: EE278B, EE279.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Zebker, H. (PI)

EE 263: Introduction to Linear Dynamical Systems (CME 263)

Applied linear algebra and linear dynamical systems with application to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: linear algebra and matrices as in MATH 103; differential equations and Laplace transforms as in EE 102A.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boyd, S. (PI)

EE 264: Digital Signal Processing

The fundamentals of digital signal processing techniques and their applications. Topics include review of two sided Z-transform, linear time invariant discrete-time systems, and sampling theory; A/D and D/A conversion, rate conversion, and oversampling techniques for ADC and DAC; filter design; quantization in digital filter implementation; discrete Fourier analysis; and parametric signal modeling. Prerequisite: EE102A and EE102B . Recommended: EE261, EE278B.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Schafer, R. (PI)

EE 265: Digital Signal Processing Laboratory

Applying 102A,B to real-world signal processing applications. Lab exercises use a programmable DSP to implement signal processing tasks. Topics: A/D conversion and quantization, sampling theorem, Z-transform, discrete-time Fourier transform, digital filter design and implementation, spectral analysis, rate conversion, wireless data communication, and OFDM receiver design. Prerequisites: 102A,B. Recommended: 261.
Terms: not given this year | Units: 3-4 | Grading: Letter (ABCD/NP)

EE 268: Introduction to Modern Optics

Geometrical optics: ray matrices, Gaussian beams, optical instruments, and radiometry. Wave nature of light: Maxwell's equations, propagation through media with varying index of refraction (e.g., fibers). Interferometry: basic principles, practical systems, and applications.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ellerbee, A. (PI)

EE 271: Introduction to VLSI Systems

Provides a quick introduction to MOS transistors and IC fabrication and then creates abstractions to allow you to create and reason about complex digital systems. It uses a switch resistor model of a transistor, uses it to model gates, and then shows how gates and physical layout can be synthesized from Verilog or SystemVerilog descriptions. Most of the class will be spent on providing techniques to create designs that can be validated, are low power, provide good performance, and can be completed in finite time. Prerequisites: 101A, 108A and 108B; familiarity with transistors, logic design, Verilog and digital system organization
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Shacham, O. (PI)

EE 272: Design Projects in VLSI Systems

Mixed signal design. Student teams create a small mixed-signal VLSI design using a modern design flow and CAD tools. The project involves writing a Verilog model of the chip, creating a testing/debug strategy for the chip, wrapping custom layout to fit into a std cell system, using synthesis and place and route tools to create the layout of your chip, and understanding all the stuff you need to do to tape-out a chip. Useful for those who plan to build a chip in their Ph.D. work. Prerequisites: EE 271 and experience in digital/analog circuit design.
Terms: alternate years, given next year | Units: 3-4 | Grading: Letter or Credit/No Credit

EE 273: Digital Systems Engineering

Electrical issues in the design of high-performance digital systems, including signaling, timing, synchronization, noise, and power distribution. High-speed signaling methods; noise in digital systems, its effect on signaling, and methods for noise reduction; timing conventions; timing noise (skew and jitter), its effect on systems, and methods for mitigating timing noise; synchronization issues and synchronizer design; clock and power distribution problems and techniques; impact of electrical issues on system architecture and design. Prerequisites: EE101A and EE108A. Recommended: EE114/214A.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 276: Introduction to Wireless Personal Communications

Frequency reuse, cellular concepts, cochannel interference, handoff. Radio propagation in and around buildings: Friis equation, multipath, narrow-band and wide-band channels, small scale and large-scale statistics, space and time signal variation. Diversity. Receiver sensitivity, sources of noise, range. Performance statistics: coverage, margin, digital modulation, adjacent channel interference, and digital error rates. Wide band channels: maximum transmission rates. Multi-server queuing and traffic: Erlang formulas. Multiple access: FDMA, TDMA, CDMA; Duplexing: TDD, FDD. Prerequisites: 242 and 278B or equivalent. Corequisite: 279 or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 278A: Probabilistic Systems Analysis (EE 178)

Introduction to probability and statistics and their role in modeling and analyzing real world phenomena. Events, sample space, and probability. Discrete random variables, probability mass functions, independence and conditional probability, expectation and conditional expectation. Continuous random variables, probability density functions, independence and expectation, derived densities. Transforms, moments, sums of independent random variables. Simple random processes. Limit theorems. Introduction to statistics: significance, estimation and detection. Prerequisites: basic calculus and linear algebra.
Terms: Aut, Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Montanari, A. (PI); Ozgur Aydin, A. (PI)

EE 278B: Introduction to Statistical Signal Processing

Review of basic probability and random variables. Random vectors and processes; convergence and limit theorems; IID, independent increment, Markov, and Gaussian random processes; stationary random processes; autocorrelation and power spectral density; mean square error estimation, detection, and linear estimation. Prerequisites: EE178/278A and linear systems and Fourier transforms at the level of EE102A,B or EE261.
Terms: Aut, Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fernández-Granda, C. (PI); Prabhakar, B. (PI)

EE 279: Introduction to Digital Communication

Digital communication is a rather unique field in engineering in which theoretical ideas have had an extraordinary impact on the design of actual systems. The course provides a basic understanding of the analysis and design of digital communication systems, building on various ideas from probability theory, stochastic processes, linear algebra and Fourier analysis. Topics include: detection and probability of error for binary and M-ary signals (PAM, QAM, PSK), receiver design and sufficient statistics, controlling the spectrum and the Nyquist criterion, bandpass communication and up/down conversion, design trade-offs: rate, bandwidth, power and error probability, coding and decoding (block codes, convolutional coding and Viterbi decoding). Prerequisites: 179 or 261, and 178 or 278
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ozgur Aydin, A. (PI)

EE 282: Computer Systems Architecture

Course focuses on how to build modern computing systems, namely notebooks, smartphones, and data centers, covering primarily their hardware architecture and certain system software aspects. For each system class, we cover the system architecture, processor technology, advanced memory hierarchy and I/O organization, power and energy management, and reliability. We will also cover topics such as interactions with system software, virtualization, solid state storage, and security. The programming assignments allow students to explore performance/energy tradeoffs when using heterogeneous hardware resources on smartphone devices. Prerequisite: EE108B. Recommended: CS 140.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kozyrakis, C. (PI)

EE 284: Introduction to Computer Networks

Structure and components of computer networks; functions and services; packet switching; layered architectures; OSI reference model; physical layer; data link layer; error control; window flow control; media access control protocols used in local area networks (Ethernet, Token Ring, FDDI) and satellite networks; network layer (datagram service, virtual circuit service, routing, congestion control, Internet Protocol); transport layer (UDP, TCP); application layer.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tobagi, F. (PI)

EE 290A: Curricular Practical Training for Electrical Engineers

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, candidacy for Engineer or Ph.D. in Electrical Engineering; for 290C, candidacy for Ph.D. degree in Electrical Engineering; for 290D, consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Nishimura, D. (PI)

EE 290B: Curricular Practical Training for Electrical Engineers

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, candidacy for Engineer or Ph.D. in Electrical Engineering; for 290C, candidacy for Ph.D. degree in Electrical Engineering; for 290D, consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Nishimura, D. (PI)

EE 290C: Curricular Practical Training for Electrical Engineers

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, candidacy for Engineer or Ph.D. in Electrical Engineering; for 290C, candidacy for Ph.D. degree in Electrical Engineering; for 290D, consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Nishimura, D. (PI)

EE 290D: Curricular Practical Training for Electrical Engineers

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, candidacy for Engineer or Ph.D. in Electrical Engineering; for 290C, candidacy for Ph.D. degree in Electrical Engineering; for 290D, consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Nishimura, D. (PI)

EE 292H: Engineering and Climate Change

The purpose of this seminar course is to help equip students and professionals with the tools to apply the engineering mindset to some of the problems that stem from climate change, in order to consider and evaluate possible interventional, remedial and adaptive approaches. This course focuses on some of the climate problems and engineering challenges that seem most critical in addressing climate change. Come join us for fascinating lectures and share ideas as to what engineering approaches maybe of most promise in this area. Very short weekly assignments (half page) to prepare for discussions with the lecturers; suggestions for further readings; and short optional student presentations on topics of interest will round out the class. May be repeated for credit.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Field, L. (PI)

EE 292I: Insanely Great Products: How do they get built?

Great products emerge from a sometimes conflict-laden process of collaboration between different functions within companies. This Seminar seeks to demystify this process via case-studies of successful products and companies. Engineering management and businesspeople will share their experiences in discussion with students. Previous companies profiled: Apple, Intel, Facebook, and Genentech -- to name a few. Previous guests include: Jon Rubinstein (NeXT, Apple, Palm), Ariel Braunstein (Flip Video), and Charlie Cheever (Facebook, Quora). Pre-requisites: None
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Obershaw, D. (PI)

EE 292J: Power Electronics

All electronics need power from somewhere, which means the applications of power electronics are everywhere. In this practical introduction to power electronics we discuss both power conversion topologies--rectifiers, switching converters, and inverters; and the details needed to get power circuits to work--magnetic design, thermal management, and control. Includes guest lectures from industry, and a field trip to the SLAC National Accelerator Laboratory to demonstrate some truly inspiring power converters. Prerequisites: EE 114.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Adams, D. (PI)

EE 292K: Intelligent Energy Projects

This course will investigate new ideas in intelligent energy systems, such as problems in Demand Response, energy management systems (EMS) in transmission or distribution systems, Locational Marginal Pricing, V2G, asset management, and grid optimization, or others. Projects will focus on analytical or algorithmic approaches, using techniques from convex optimization, control, reinforcement learning, and modern Big Data programming techniques, etc. Students are expected to present their results in oral and written form. To provide sufficient instructor/student interaction, class size is limited and consent of the instructor will be required for the class enrollment.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Gorinevsky, D. (PI); O'Neill, D. (PI)

EE 292L: Nanomanufacturing

Fundamentals of nanomanufacturing technology and applications. Topics include recent developments in process technology, lithography and patterning. Technology for FinFET transistors, NAND flash and 3D chips. Manufacturing of LEDs, thin film and crystalline solar cells. Flip classroom model is used supplementing classroom lectures with short videos. Guest speakers include distinguished engineers, entrepreneurs and venture capitalists actively engaged in nanomanufacturing. Prerequisite: background in device physics and process technology. Recommended: EE116, EE216, EE212
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Nainani, A. (PI); Saraswat, K. (PI)

EE 292M: Parallel Processors Beyond Multi-Core Processing

The current parallel computing research emphasizes multi-cores, but there are alterna-tive array processors with significant potential. This hands-on seminar focuses on SIMD (Single-Instruction, Multiple-Data) massively parallel processors, with weekly programming assignments. Topics: Flynn's Taxonomy, parallel architectures, the K-SIMD simulator, principles of SIMD programming, parallel sorting with sorting networks, string comparison with dynamic programming (edit distance, Smith-Waterman), arbitrary-precision operations with fixed-point numbers, reductions, vector and matrix multiplication, asynchronous algorithms on SIMD ("SIMD Phase Programming Model"), Mandelbrot set, analysis of parallel performance. Prerequisites: EE108B and EE282. Recommended: CS140.
Terms: Win | Units: 2 | Grading: Letter (ABCD/NP)

EE 293A: Fundamentals of Energy Processes (ENERGY 293A)

For seniors and graduate students. Thermodynamics, heat engines, thermoelectics, biomass. Recommended: MATH 41, 43; PHYSICS 41, 43, 45
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit

EE 293B: Fundamentals of Energy Processes (ENERGY 293B)

For seniors and graduate students. Fuel cells. Production of hydrogen: electrolytic, chemical, thermolytic, photolytic. Hydrogen storage: hydrides. Photoelectric converters; photo-thermovoltaic converters. Wind turbines. Recommended: EE 293A; MATH 41; PHYSICS 41, 43, 45
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

EE 300: Master's Thesis and Thesis Research

Independent work under the direction of a department faculty. Written thesis required for final letter grade. The continuing grade 'N' is given in quarters prior to thesis submission. See 390 if a letter grade is not appropriate. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Bahai, A. (PI); Baker, M. (PI); Bambos, N. (PI); Beasley, M. (PI); Binford, T. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Chidsey, C. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); DaRosa, A. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Eshleman, V. (PI); Fan, S. (PI); Flynn, M. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Heeger, D. (PI); Helliwell, R. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Kailath, T. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kiehl, R. (PI); Kim, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lee, T. (PI); Leeson, D. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Luckham, D. (PI); Macovski, A. (PI); Manoharan, H. (PI); Marcus, B. (PI); McCluskey, E. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Popelka, G. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Zebker, H. (PI)

EE 309: Semiconductor Memory Devices and Technology

Focus is on the device physics and operation principles of nanoelectric devices. Topics identified by the International Technology Roadmap for Semiconductors, emerging research devices sec-tion; see http://www.itrs.net. Non-silicon-based devices such as carbon nanotubes, grapheme, semiconductor na-nowires, and molecular devices; and non-FET based devices such as single electron transistors (SET) and resonant tunneling diodes (RTD). Logic and memory devices are covered. Prerequisite: undergra-duate device physics, EE 222, EE 216. Recommended: EE 212, EE 223, EE 228, EE 311, and EE 316 Offered Alternate years.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

EE 310: Integrated Circuits Technology and Design Seminar

State-of-the-art micro- and nanoelectronics, nanotechnology, advanced materials, and nanoscience for device applications. Prerequisites: EE216, EE316.May be repeated for credit
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

EE 311: Advanced Integrated Circuits Technology

What are the practical and fundamental limits to the evolution of the technology of modern MOS devices and interconnects? How are modern devices and circuits fabricated and what future changes are likely? Advanced techniques and models of MOS devices and back-end (interconnect and contact) processing. What are future device structures and materials to maintain progress in integrated electronics? MOS front-end and back-end process integration. Prerequisites: EE212, EE216 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Saraswat, K. (PI)

EE 313: Digital MOS Integrated Circuits

Looks a little more deeply at how digital circuits operate, what makes a gate digital, and how to "cheat" to improve performance or power. To aid this analysis we create a number of different models for MOS transistors and choose the simplest one that can explain our the circuit's operation, using both hand and computer analysis. We explore static, dynamic, pulse-mode, and current mode logic, and show how they are are used in SRAM design. Topics include sizing for min delay, noise and noise margins, power dissipation. The class uses memory design (SRAM) as a motivating example. DRAM and EEPROM design issues are also covered. Prerequisites: 101B, 108A. Recommended: 271.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Lim, B. (PI)

EE 314A: RF Integrated Circuit Design

Design of RF integrated circuits for communications systems, primarily in CMOS. Topics: the design of matching networks and low-noise amplifiers at RF, mixers, modulators, and demodulators; review of classical control concepts necessary for oscillator design including PLLs and PLL-based frequency synthesizers. Design of low phase noise oscillators. Design of high-efficiency (e.g., class E, F) RF power amplifiers, coupling networks. Behavior and modeling of passive and active components at RF. Narrowband and broadband amplifiers; noise and distortion measures and mitigation methods. Overview of transceiver architectures. Prerequisite: EE214B.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Lee, T. (PI)

EE 314B: Advanced RF Integrated Circuit Design

Analysis and design of modern communication circuits and systems with emphasize on design techniques for high-frequency (into mm-wave) ICs. Topics include MOS, bipolar, and BiCMOS high-frequency integrated circuits, including power amplifiers, extremely wideband amplifiers, advanced oscillators, phase-locked loops and frequency-translation circuits. Design techniques for mm-wave silicon ICs (on-chip low-loss transmissions lines, unilateralization techniques, in-tegrated antennas, harmonic generation, etc) will also be studied. Prerequisite: EE314
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Arbabian, M. (PI)

EE 315A: VLSI Signal Conditioning Circuits

Design and analysis of integrated circuits for active filters, precision gain stages, and sensor interfaces in CMOS VLSI technology. Operational transconductance amplifiers; sampled-data and continuous-time analog filters. Analysis of noise and amplifier imperfections; compensation techniques such as correlated double sampling. Sensor interfaces for micro-electromechanical and biomedical applications. Layout techniques for analog integrated circuits. Prerequisites: EE214B.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Murmann, B. (PI)

EE 315B: VLSI Data Conversion Circuits

Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in CMOS VLSI technology. Fundamental circuit elements such as sampling circuits and voltage comparators. Circuits and architectures for Nyquist-rate and oversampling analog-to-digital and digital-to-analog conversion; digital decimation and interpolation filters. Examples of calibration and digital enhancement techniques. Prerequisite: EE 214B. Recommended: EE 315A.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Murmann, B. (PI)

EE 316: Advanced VLSI Devices

In modern VLSI technologies, device electrical characteristics are sensitive to structural details and therefore to fabrication techniques. How are advanced VLSI devices designed and what future changes are likely? What are the implications for device electrical performance caused by fabrication techniques? Physical models for nanometer scale structures, control of electrical characteristics (threshold voltage, short channel effects, ballistic transport) in small structures, and alternative device structures for VLSI. Prerequisites: 212 and 216, or equivalent.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Wong, P. (PI)

EE 319: Advanced Nanoelectronic Devices and Technology

Recent advances in materials science, device physics and structures, and processing technology, to extend VLS1 device scaling towards atomistic and quantum-mechanical physics boundaries. Topics include: mobility-enhancement techniques; nanomaterial structures including tube, wire, beam, and crystal; conducting polymer; 3D FET; gate-wraparound FET; nonvolatile memory phenomena and devices; self-assembly; flash annealing; plasma doping; and nano pattering. Prerequisites: 216, 316.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Nishi, Y. (PI)

EE 320: Nanoelectronics

Focus is on the device physics and operation principles of nanoelectric devices. Topics identified by the International Technology Roadmap for Semiconductors, emerging research devices section; see http://www.itrs.net. Non-silicon-based devices such as carbon nanotubes, grapheme, semiconductor nanowires, and molecular devices; and non-FET based devices such as single electron transistors (SET) and resonant tunneling diodes (RTD). Logic and memory devices. Offered alternative years. Prerequisites: undergraduate device physics, EE 222, 216. Recommended: EE 223, 228, or 316.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Wong, P. (PI)

EE 327: Properties of Semiconductor Materials

Modern semiconductor devices and integrated circuits are based on unique energy band, carrier transport, and optical properties of semiconductor materials. How to choose these properties for operation of semiconductor devices. Emphasis is on quantum mechanical foundations of the properties of solids, energy bandgap engineering, semi-classical transport theory, semi-conductor statistics, carrier scattering, electro-magneto transport effects, high field ballistic transport, Boltzmann transport equation, quantum mechanical transitions, optical absorption, and radiative and non-radiative recombination that are the foundations of modern transistors and optoelectronic devices. Prerequisites: EE216 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Harris, J. (PI)

EE 331: Biophotonics: Light in Medicine and Biology

Current topics and trends in the use of light in medicine and for advanced microscopy. Course begins with a review of relevant optical principles (basic physics required). Key topics include: light-tissue interactions; sensing and spectroscopy; contrast-enhanced imaging; super-resolution and label-free microscopy; medical applications of light for diagnostics, in-vivo imaging, and therapy; nanophotonics and array technologies. Open to non-majors; programming experience (Matlab and/or C) required.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ellerbee, A. (PI)

EE 334: Micro and Nano Optical Device Design

Lecture and project course on design and analysis of optical devices with emphasis on opportunities and challenges created by scaling to the micrometer and nanometer ranges. The emphasis is on fundamentals, combined with some coverage of practical implementations. Prerequisite: EE 242 or equivalent
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Solgaard, O. (PI)

EE 336: Nanophotonics (MATSCI 346)

Recent developments in micro- and nanophotonic materials and devices. Basic concepts of photonic crystals. Integrated photonic circuits. Photonic crystal fibers. Superprism effects. Optical properties of metallic nanostructures. Sub-wavelength phenomena and plasmonic excitations. Meta-materials. Prerequisite: Electromagnetic theory at the level of 242.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

EE 340: Optical Micro- and Nano-Cavities

Optical micro- and nano-cavities and their device applications. Types of optical cavities (microdisks, microspheres, photonic crystal cavities, plasmonic cavities), and their electromagnetic properties, design, and fabrication techniques. Cavity quantum electrodynamics: strong and weak-coupling regime, Purcell factor, spontaneous emission control. Applications of optical cavities, including low-threshold lasers, optical modulators, quantum information processing devices, and bio-chemical sensors.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Vuckovic, J. (PI)

EE 345: Optical Fiber Communication Laboratory

Experimental techniques in optical fiber communications and networking. Experimental investigation of key optical communications components including fibers, lasers, modulators, photodiodes, optical amplifiers, and WDM multiplexers and demultiplexers. Fundamental optical communications systems techniques: eye diagrams, BER measurements, experimental evaluation of nonlinearties. Prerequisites: Undergraduate physics and optics.
Terms: alternate years, given next year | Units: 3 | Grading: Letter (ABCD/NP)

EE 346: Introduction to Nonlinear Optics

Wave propagation in anisotropic, nonlinear, and time-varying media. Microscopic and macroscopic description of electric dipole susceptibilities. Free and forced waves-phasematching; slowly varying envelope approximation-dispersion, diffraction, space-time analogy; harmonic generation; frequency conversion; parametric amplification and oscillation; electro-optic light modulation; nonlinear processes in optical fibers. Prerequisites: EE 141, EE 242.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Fejer, M. (PI)

EE 348: Advanced Optical Fiber Communications

Optical amplifiers: gain, saturation, noise. Semiconductor amplifiers. Erbium-doped fiber amplifiers. System applications: preamplified receiver performance, amplifier chains. Raman amplifiers, lumped vs. distributed amplification. Group-velocity dispersion management: dispersion-compensating fibers, filters, gratings. Interaction of dispersion and nonlinearity, dispersion maps. Multichannel systems. Wavelength-division multiplexing components: filters, multiplexers. WDM systems, crosstalk. Time, subcarrier, code and polarization-division multiplexing. Comparison of modulation techniques: differential phase-shift keying, phase-shift keying, quadrature-amplitude modulation. Comparison of detection techniques: noncoherent, differentially coherent, coherent. Prerequisite: 247.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 355: Imaging Radar and Applications (GEOPHYS 265)

Radar remote sensing, radar image characteristics, viewing geometry, range coding, synthetic aperture processing, correlation, range migration, range/Doppler algorithms, wave domain algorithms, polar algorithm, polarimetric processing, interferometric measurements. Applications: surfafe deformation, polarimetry and target discrimination, topographic mapping surface displacements, velocities of ice fields. Prerequisites: EE261. Recommended: EE254, EE278B, EE279.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 359: Wireless Communications

This course will cover advanced topics in wireless communications for voice, data, and multimedia. Topics include: an overview of current and future wireless systems; wireless channel models including path loss, shadowing, and statistical multipath channel models; fundamental capacity limits of wireless channels; digital modulation and its performance in fading and intersymbol interference; techniques to combat fading including adaptive modulation, diversity, and multiple antenna systems (MIMO); techniques to combat intersymbol interference including equalization, multicarrier modulation (OFDM), and spread spectrum; and an overview of wireless network design. Prerequisite: 279 or instructor consent.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Goldsmith, A. (PI)

EE 360: Multiuser Wireless Systems and Networks

Design, analysis, and fundamental limits. Topics include multiuser channel capacity, multiple and random access techniques, interference mitigation, cellular system design, ad hoc wireless network design, sensor networks, "green" wireless networks, cognitive radios, and cross-layer design. Prerequisite: EE 359.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

EE 361: Principles of Cooperation in Wireless Networks

Information theory forms the basis for the design of all modern day communication systems. The original theory was primarily point-to-point, studying how fast information can flow across an isolated noisy communication channel. Until recently, there has been only limited success in extending the theory to a network of interacting nodes. Progress has been made in the past decade driven by engineering interest in wireless networks. The course provides a unified overview of this recent progress made in information theory of wireless networks. Starting with an overview of the capacity of fading and multiple-antenna wireless channels, we aim to answer questions such as: What is the optimal way for users to cooperate and exchange information in a wireless network? How much benefit can optimal cooperation provide over traditional communication architectures? How can cooperation help to deal with interference between multiple wireless transmissions? Prerequisites: 376A
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ozgur Aydin, A. (PI)

EE 364A: Convex Optimization I (CME 364A, CS 334A)

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, EE178/278A.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boyd, S. (PI)

EE 364B: Convex Optimization II (CME 364B)

Continuation of 364. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications. Substantial project. Prerequisite: 364A.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 365: Stochastic Decision Models (MS&E 251)

Efficient formulation and computational solution of sequential decision problems under uncertainty. Markov decision chains and stochastic programming. Maximum expected present value and rate of return. Optimality of simple policies: myopic, linear, index, acceptance limit, and (s,S). Optimal stationary and periodic infinite-horizon policies. Applications to investment, options, overbooking, inventory, production, purchasing, selling, quality, repair, sequencing, queues, capacity, transportation. MATLAB is used. Prerequisites: probability, linear programming.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

EE 365: STOCHASTIC CONTROL

Introduction to stochastic control, with applications taken from a variety of areas including supply-chain optimization, advertising, finance, dynamic resource allocation, caching, and traditional automatic control. Markov decision processes, optimal policy with full state information for finite-horizon case, infinite-horizon discounted, and average stage cost problems. Bellman value function, value iteration, and policy iteration. Approximate dynamic programming. Linear quadratic stochastic control. Prerequisites: EE 263, EE 278A or equivalent
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

EE 368: Digital Image Processing (CS 232)

Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, wavelets, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students write and investigate image processing algorithms in Matlab. Term project. Prerequisites: EE261, EE278B.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Girod, B. (PI)

EE 369A: Medical Imaging Systems I

Imaging internal structures within the body using high-energy radiation studied from a systems viewpoint. Modalities covered: x-ray, computed tomography, and nuclear medicine. Analysis of existing and proposed systems in terms of resolution, frequency response, detection sensitivity, noise, and potential for improved diagnosis. Prerequisite: EE 261
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Nishimura, D. (PI)

EE 369B: Medical Imaging Systems II

Imaging internal structures within the body using non-ionizing radiation studied from a systems viewpoint. Modalities include ultrasound and magnetic resonance. Analysis of ultrasonic systems including diffraction and noise. Analysis of magnetic resonance systems including physics, Fourier properties of image formation, and noise. Prerequisite: EE 261
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Nishimura, D. (PI)

EE 371: Advanced VLSI Circuit Design

Design of high-performance digital systems, the things that cause them to fail, and how to avoid these problems. Topics will focus on current issues including: wiring resistance and how to deal with it, power and Gnd noise and regulation, clock (or asynchronous) system design and how to minimize clocking overhead, high-speed I/O design, energy minimization including leakage control, and structuring your Verilog code to result in high-performance, low energy systems. Extensive use of modern CAD tools. Prerequisites: 271 and 313, or consent of instructor.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

EE 373A: Adaptive Signal Processing

Learning algorithms for adaptive digital filters. Self optimization. Wiener filter theory. Quadratic performance functions, their eigenvectors and eigenvalues. Speed of convergence. Asymptotic performance versus convergence rate. Applications of adaptive filters to statistical prediction, process modeling, adaptive noise canceling, adaptive antenna arrays, adaptive inverse control, and equalization and echo cancelling in modems. Theoretical and experimental research projects in adaptive filter theory, communications, and audio systems. Biomedical research projects, supervised jointly by EE and Medical School faculty. Recommended: EE263, EE264, EE278B.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Widrow, B. (PI)

EE 376A: Information Theory (STATS 376A)

The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178/278A or STATS 116, or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Weissman, I. (PI)

EE 378A: Statistical Signal Processing

Random signals in electrical engineering. Discrete-time random processes: stationarity and ergodicity, covariance sequences, power spectral density, parametric models for stationary processes. Fundamentals of linear estimation: minimum mean squared error estimation, optimum linear estimation, orthogonality principle, the Wold decomposition. Causal linear estimation of stationary processes: the causal Wiener filter, Kalman filtering. Parameter estimation: criteria of goodness of estimators, Fisher information, Cramer-Rao inequality, Chapman-Robbins inequality, maximum likelihood estimation, method of moments, consistency, efficiency. ARMA parameter estimation: Yule-Walker equations, Levinson-Durbin algorithm, least squares estimation, moving average parameter estimation, modified Yule-Walker method for model order selection. Spectrum estimation: sample covariances, covariance estimation, Bartlett formula, periodogram, periodogram averaging, windowed periodograms. Prerequisites: EE 278B
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Weissman, I. (PI)

EE 378B: Inference, Estimation, and Information Processing

Techniques and models for signal, data and information processing, with emphasis on incomplete data, non-ordered index sets and robust low-complexity methods. Linear models; regularization and shrinkage; dimensionality reduction; streaming algorithms; sketching; clustering, search in high dimension; low-rank models; principal component analysis. Applications include: positioning from pairwise distances; distributed sensing; measurement/traffic monitoring in networks; finding communities/clusters in networks; recommendation systems; inverse problems. Prerequisites: EE278B and EE263 or equivalent. Recommended but not required: EE378A
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Montanari, A. (PI)

EE 379: Digital Communication

Modulation methods and bandwidth requirements, baseband and passband system analysis, minimum-probability-of-error and maximum-likelihood detection, error-probability analysis, intersymbol interference, maximum-likelihood sequence detection, equalization methods, orthogonal frequency-division multiplexing. Prerequisite: EE102B, EE278B
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Kahn, J. (PI)

EE 380: Colloquium on Computer Systems

Live presentations of current research in the design, implementation, analysis, and applications of computer systems. Topics range over a wide range and are different every quarter. Topics may include fundamental science, mathematics, cryptography, device physics, integrated circuits, computer architecture, programming, programming languages, optimization, applications, simulation, graphics, social implications, venture capital, patent and copyright law, networks, computer security, and other topics of related to computer systems. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

EE 382C: Interconnection Networks

The architecture and design of interconnection networks used to communicate from processor to memory, from processor to processor, and in switches and routers. Topics: network topology, routing methods, flow control, router microarchitecture, and performance analysis. Enrollment limited to 30. Prerequisite: 282.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

EE 382E: Advanced Multi-Core Systems (CS 316)

In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Students will become familiar with complex trade-offs between performance-power-complexity and hardware-software interactions. A central part of CS316 is a project on an open research question on multi-core technologies. Prerequisites: EE 108B. Recommended: CS 149, EE 282.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

EE 384A: Internet Routing Protocols and Standards

Local area networks addressing and switching; IEEE 802.1 bridging protocols (transparent bridging, virtual LANs). Internet routing protocols: interior gateways (RIP, OSPF) and exterior gateways (BGP); multicast routing; multiprotocol label switching (MPLS). Routing in mobile networks: Mobile IP, Mobile Ad Hoc Networks (MANET), Wireless Mesh Networks. Prerequisite: EE 284 or CS 144.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tobagi, F. (PI)

EE 384C: Wireless Local and Wide Area Networks

Characteristics of wireless communication: multipath, noise, and interference. Communications techniques: spread-spectrum, CDMA, and OFDM. IEEE 802.11 physical layer specifications: FHSS, DSSS, IEEE 802.11b (CCK), and 802.11a/g (OFDM). IEEE 802.11 media access control protocols: carrier sense multiple access with collision avoidance (CSMA/CA), point coordination function (PCF), IEEE802.11e for differentiated services. IEEE 802.11 network architecture: ad hoc and infrastructure modes, access point functionality. Management functions: synchronization, power management and association. IEEE 802.11s Mesh Networks. IEEE 802.16 (WiMAX) network architecture and protocols: Physical Layer (OFDMA) and Media Access Control Layer. Current research papers in the open literature. Prerequisite: EE 284 or CS 244A.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tobagi, F. (PI)

EE 384E: Networked Wireless Systems (CS 244E)

Design and implementation of wireless networks and mobile systems. The course will commence with a short retrospective of wireless communication and initially touch on some of the fundamental physical layer properties of various wireless communication technologies. The focus will then shift to design of media access control and routing layers for various wireless systems. The course will also examine adaptations necessary at transport and higher layers to cope with node mobility and error-prone nature of the wireless medium. Finally, it will conclude with a brief overview of other related issues including emerging wireless/mobile applications. Prerequisites: EE 284
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 384M: Network Science

Modern large-scale networks consist of (i) Information Networks, such as the Web and Social Networks, and (ii) Data Centers, which are networks interconnecting computing and storage elements for servicing the users of an Information Network. This course is concerned with the mathematical models and the algorithms used in Information Networks and Data Centers. Prerequisite: EE178/278A or CS365.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 384S: Performance Engineering of Computer Systems & Networks

Modeling and control methodologies for high-performance network engineering, including: Markov chains and stochastic modeling, queueing networks and congestion management, dynamic programming and task/processor scheduling, network dimensioning and optimization, and simulation methods. Applications for design of high-performance architectures for wireline/wireless networks and the Internet, including: traffic modeling, admission and congestion control, quality of service support, power control in wireless networks, packet scheduling in switches, video streaming over wireless links, and virus/worm propagation dynamics and countermeasures. Enrollment limited to 30. Prerequisites: basic networking technologies and probability.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bambos, N. (PI)

EE 384X: Packet Switch Architectures

The theory and practice of designing packet switches, such as Internet routers, and Ethernet switches. Introduction: evolution of switches and routers. Output queued switches: motivation and methods for providing bandwidth and delay guarantees. Switching: output queueing, parallelism in switches, distributed shared memory switches, input-queued switches, combined input-output queued switches, how to make fast packet buffers, buffered crossbar switches. Scheduling input queued crossbars: connections with bipartite graph matching, algorithms for 100% throughput, practical algorithms and heuristics. Looking forward: Architectures and switches for data center networks. Prerequisites: EE284 or CS 244A. Recommended: EE 178/278A or EE 278B or STAT 116.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

EE 385A: Robust and Testable Systems Seminar

Student/faculty discussions of research problems in the design of reliable digital systems. Areas: fault-tolerant systems, design for testability, production testing, and system reliability. Emphasis is on student presentations and Ph.D. thesis research. May be repeated for credit. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr | Units: 1-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

EE 386: Robust System Design

Causes of system malfunctions; techniques for building robust systems that avoid or are resilient to such malfunctions through built-in error detection and correction, prediction, self-test, self-recovery, and self-repair; case studies and new research problems. Prerequisites: 108A,B, 282.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

EE 387: Algebraic Error Control Codes

Theory and implementation of algebraic codes for detection and correction of random and burst errors. Introduction to finite fields. Linear block codes, cyclic codes, Hamming codes, BCH codes, Reed-Solomon codes. Decoding algorithms for BCH and Reed-Solomon codes. Prerequisites: elementary probability, linear algebra.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Gill, J. (PI)

EE 390: Special Studies or Projects in Electrical Engineering

Independent work under the direction of a faculty member. Individual or team activities may involve lab experimentation, design of devices or systems, or directed reading. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Arbabian, M. (PI); Bahai, A. (PI); Bambos, N. (PI); Boahen, K. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); DaRosa, A. (PI); Dai, H. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Ellerbee, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Fan, S. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Giovangrandi, L. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Hellman, M. (PI); Helms, C. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Katti, S. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lee, T. (PI); Leeson, D. (PI); Levin, C. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Manoharan, H. (PI); McCluskey, E. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Ozgur Aydin, A. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Popelka, G. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Zebker, H. (PI); Chakraborty, S. (TA)

EE 391: Special Studies and Reports in Electrical Engineering

Independent work under the direction of a faculty member; written report or written examination required. Letter grade given on the basis of the report; if not appropriate, student should enroll in 390. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Abel, J. (PI); Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Arbabian, M. (PI); Bahai, A. (PI); Bambos, N. (PI); Bent, S. (PI); Boahen, K. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Brongersma, M. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Chmelar, E. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); Cui, Y. (PI); DaRosa, A. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Ellerbee, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Fan, S. (PI); Fejer, M. (PI); Frank, M. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Giovangrandi, L. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Hellman, M. (PI); Helms, C. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Katti, S. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lauben, D. (PI); Lee, T. (PI); Leeson, D. (PI); Levin, C. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Manoharan, H. (PI); McCluskey, E. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Moerner, W. (PI); Montanari, A. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Ozgur Aydin, A. (PI); Palanker, D. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Popelka, G. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Yang, D. (PI); Zebker, H. (PI)

EE 392F: Logic Synthesis of VLSI Circuits

Similar to former 318. Solving logic design problems with CAD tools for VLSI circuits. Exact and heuristic algorithms for logic synthesis. Representation and optimization of combinational logic functions (encoding problems, binary decision diagrams) and of multiple-level networks (algebraic and Boolean methods, don't-care set computation, timing verification, and optimization);and modeling and optimization of sequential functions and networks (retiming), semicustom libraries, and library binding. Prerequisites: familiarity with logic design, algorithm development, and programming.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 392N: INTELLIGENT ENERGY SYSTEMS

The key systems engineering steps for design of automated systems in application to of existing and future intelligent energy systems. Existing design approaches and practices for the energy systems. Every second lecture of the course will be a guest lecture discussing the communication system design for a certain type of energy system. They will alternate with guest lectures discuss-ing the on-line analytical functions.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Gorinevsky, D. (PI); O'Neill, D. (PI)

EE 392P: Nanoscale Device Physics

The course develops an understanding of nanoscale devices relevant to information manipulation: electronic drawing on ballistic, single electron, quantum confinement, and phase transitions such as ferroelectric, metal-insulator, and structural; magnetic employing field-switching, spin-torque and spin Hall; photonic using photonic bandgaps and non-linearities; and mechanical employing deflection, torsion and resonance. The physical phenomena that these connect to are electron-phonon effects in dielectrics, mesoscopic and single-electron phenomena, phase transitions, magnetic switching, spin-torque effect, Casimir effect, plasmonics, and their coupled interactions. Prerequisites: EE 216 or equivalent. Recommended: EE 222.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tiwari, S. (PI)

EE 392T: Seminar in Chip Test and Debug

Seminars by industry professionals in digital IC manufacturing test and silicon debug. Topics include yield and binsplit modeling, defect types and detection, debug hardware, physical analysis, and design for test/debug circuits. Case studies of silicon failures. Prerequisite: basic digital IC design (271 or 371).
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Stinson, J. (PI)

EE 395: Electrical Engineering Instruction: Practice Teaching

Open to advanced EE graduate students who plan to make teaching their career. Students conduct a section of an established course taught in parallel by an experienced instructor. Enrollment limited.
Terms: Aut, Win, Spr | Units: 1-15 | Grading: Satisfactory/No Credit

EE 398A: Image and Video Compression

Replaces EE398. The principles of source coding for the efficient storage and transmission of still and moving images. Entropy and lossless coding techniques. Run-length coding and fax compression. Arithmetic coding. Rate-distortion limits and quantization. Lossless and lossy predictive coding. Transform coding, JPEG. Subband coding, wavelets, JPEG2000. Motion-compensated coding, MPEG. Students investigate image and video compression algorithms in Matlab or C. Term project. Prerequisites: EE261, EE278B.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

EE 400: Thesis and Thesis Research

Limited to candidates for the degree of Engineer or Ph.D.May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Bahai, A. (PI); Bambos, N. (PI); Boahen, K. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); DaRosa, A. (PI); Dai, H. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Fan, S. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Giovangrandi, L. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Helms, C. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Katti, S. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lee, T. (PI); Leeson, D. (PI); Levin, C. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Manoharan, H. (PI); McCluskey, E. (PI); McConnell, M. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Ozgur Aydin, A. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Popelka, G. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Zebker, H. (PI)

EE 402A: Topics in International Technology Management

Theme for Autumn 2012 is "Green Technologies in Transportation: Recent Developments from Asia." Technology and business trends, innovations, and opportunities in Asia and Japan, e.g. new materials, fuels, and energy storage for vehicles; automobile and aircraft design; smart grids and intelligent transportation systems; mobile mesh networks, etc.. Implications for US firms and researchers. Distinguished speakers from industry and government.May be repeated for credit.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Dasher, R. (PI)

EE 402T: Entrepreneurship in Asian High-Tech Industries

Distinctive patterns and challenges of entrepreneurship in Asia; update of business and technology issues in the creation and growth of start-up companies in major Asian economies. Distinguished speakers from industry, government, and academia. Course may be repeated for credit.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Dasher, R. (PI)

EE 410: Integrated Circuit Fabrication Laboratory

Fabrication, simulation, and testing of a submicron CMOS process. Practical aspects of IC fabrication including silicon wafer cleaning, photolithography, etching, oxidation, diffusion, ion implantation, chemical vapor deposition, physical sputtering, and electrical testing. Students also simulate the CMOS process using process simulator TSUPREM4 of the structures and electrical parameters that should result from the process flow. Taught in the Stanford Nanofabrication Facility (SNF). Preference to students pursuing doctoral research program requiring SNF facilities. Enrollment limited to 20. Prerequisites: EE 212, EE 216, consent of instructor.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Saraswat, K. (PI)

EE 412: Advanced Nanofabrication Laboratory

Experimental projects and seminars on integrated circuit fabrication using epitaxial, oxidation, diffusion, evaporation, sputtering, and photolithographic processes with emphasis on techniques for achieving advanced device performance. May be repeated for additional credit. Prerequisites: ENGR341 or EE410 or consent of instructor.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

EE 414: RF Transceiver Design Laboratory

Students design, build, and test GHz transceivers using microstrip construction techniques and discrete components. The design, construction, and experimental characterization of representative transceiver building blocks: low noise amplifiers (LNAs), diode ring mixers, PLL-based frequency synthesizers, voltage-controlled oscillators (VCOs), power amplifiers (PAs), and microstrip filters and patch antennas. The characteristics of passive microstrip components (including interconnect). Emphasis is on a quantitative reconciliation of theoretical predictions and extensive experimental measurements performed with spectrum and network analyzers, time-domain reflectometers (TDRs), noise figure meter and phase noise analyzers. Prerequisites: EE 314, EE 344.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

EE 418: Topics in Neuroengineering

Neuroscience and electrical engineering, focusing on principles and theory in modern neural prosthetic systems (brain-computer or brain-machine interfaces). Electrical properties of neurons, information encoding, neural measurement techniques and technology, processing electronics, information decoding and estimators, and statistical data analysis. Prerequisites: EE 214B, EE 278B.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

EE 464: Semidefinite Optimization and Algebraic Techniques

This course focuses on recent developments in optimization, specifically on the use of convex optimization to address problems involving polynomial equations and inequalities. The course covers approaches for finding both exact and approximate solutions to such problems. We will discuss the use of duality and algebraic methods to find feasible points and certificates of infeasibility, and the solution of polynomial optimization problems using semidefinite programming. The course covers theoretical foundations as well as algorithms and their complexity. Prerequisites: EE364A or equivalent course on convex optimization.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Lall, S. (PI)

EE 469B: RF Pulse Design for Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) and spectroscopy (MRS) based on the use of radio frequency pulses to manipulate magnetization. Analysis and design of major types of RF pulses in one and multiple dimensions, analysis and design of sequences of RF pulses for fast imaging, and use of RF pulses for the creation of image contrast in MRI. Prerequisite: 369B.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pauly, J. (PI)

EE 476: Network Information Theory

Network information theory deals with the fundamental limits on information flow in networks and the optimal coding schemes that achieve these limits. It aims to extend Shannon's point-to-point information theory and the Ford-Fulkerson max-flow min-cut theorem to networks with multiple sources and destinations. The course presents the basic results and tools in the field in a simple and unified manner. Topics covered include: multiple access channels, broadcast channels, interference channels, channels with state, distributed source coding, multiple description coding, network coding, relay channels, interactive communication, and noisy network coding. Prerequisites: EE376A.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: El-Gamal, A. (PI)

EE 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Bahai, A. (PI); Bambos, N. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); Cui, Y. (PI); DaRosa, A. (PI); Dally, W. (PI); Dasher, R. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Fan, S. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, J. (PI); Gill, J. (PI); Giovangrandi, L. (PI); Girod, B. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gray, R. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lee, T. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); McCluskey, E. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitra, S. (PI); Moerner, W. (PI); Montanari, A. (PI); Murmann, B. (PI); Narasimha, M. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Palanker, D. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Popelka, G. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Solgaard, O. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tyler, G. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Walt, M. (PI); Wang, S. (PI); Weissman, I. (PI); Widom, J. (PI); Widrow, B. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Yamamoto, Y. (PI); Zebker, H. (PI)

EE 802: TGR Dissertation

May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
Instructors: Aghajan, H. (PI); Allison, D. (PI); Apostolopoulos, J. (PI); Bahai, A. (PI); Baker, M. (PI); Bambos, N. (PI); Beasley, M. (PI); Bent, S. (PI); Binford, T. (PI); Boneh, D. (PI); Bosi-Goldberg, M. (PI); Boyd, S. (PI); Bravman, J. (PI); Bube, R. (PI); Byer, R. (PI); Cheriton, D. (PI); Chidsey, C. (PI); Cioffi, J. (PI); Cover, T. (PI); Cox, D. (PI); DaRosa, A. (PI); Dally, W. (PI); Dasher, R. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dutton, R. (PI); El-Gamal, A. (PI); Ellerbee, A. (PI); Emami-Naeini, A. (PI); Enge, P. (PI); Engler, D. (PI); Eshleman, V. (PI); Fan, S. (PI); Flynn, M. (PI); Franklin, G. (PI); Fraser-Smith, A. (PI); Garcia-Molina, H. (PI); Gibbons, F. (PI); Gibbons, J. (PI); Gill, J. (PI); Girod, B. (PI); Glover, G. (PI); Goldsmith, A. (PI); Goodman, J. (PI); Gorinevsky, D. (PI); Gray, R. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Harris, J. (PI); Harris, S. (PI); Hashemi, H. (PI); Heeger, D. (PI); Helliwell, R. (PI); Helms, C. (PI); Hennessy, J. (PI); Hesselink, L. (PI); Horowitz, M. (PI); Howe, R. (PI); Inan, U. (PI); Kahn, J. (PI); Kailath, T. (PI); Katti, S. (PI); Kazovsky, L. (PI); Khuri-Yakub, B. (PI); Kiehl, R. (PI); Kim, B. (PI); Kino, G. (PI); Kovacs, G. (PI); Koza, J. (PI); Kozyrakis, C. (PI); Lall, S. (PI); Lam, M. (PI); Lee, T. (PI); Leeson, D. (PI); Levin, C. (PI); Levis, P. (PI); Levoy, M. (PI); Linscott, I. (PI); Long, E. (PI); Luckham, D. (PI); Macovski, A. (PI); Manoharan, H. (PI); Marcus, B. (PI); McCluskey, E. (PI); McConnell, M. (PI); McKeown, N. (PI); Melen, R. (PI); Meng, T. (PI); Miller, D. (PI); Mitchell, J. (PI); Mitra, S. (PI); Moerner, W. (PI); Montanari, A. (PI); Murmann, B. (PI); Napel, S. (PI); Narasimha, M. (PI); Ng, A. (PI); Nishi, Y. (PI); Nishimura, D. (PI); Olukotun, O. (PI); Osgood, B. (PI); Paulraj, A. (PI); Pauly, J. (PI); Pease, R. (PI); Pelc, N. (PI); Peumans, P. (PI); Pianetta, P. (PI); Plummer, J. (PI); Poon, A. (PI); Powell, J. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Quate, C. (PI); Reis, R. (PI); Rosenblum, M. (PI); Saraswat, K. (PI); Saxena, N. (PI); Shahidi, R. (PI); Shaw, H. (PI); Shen, Z. (PI); Shenoy, K. (PI); Siegel, M. (PI); Siegman, A. (PI); Smith, J. (PI); Solgaard, O. (PI); Solomon, G. (PI); Spielman, D. (PI); Stinson, J. (PI); Su, D. (PI); Thompson, N. (PI); Thrun, S. (PI); Tobagi, F. (PI); Tomlin, C. (PI); Tyler, G. (PI); Ullman, J. (PI); Van Roy, B. (PI); Vishnu, M. (PI); Vuckovic, J. (PI); Wakerly, J. (PI); Walt, M. (PI); Wandell, B. (PI); Wang, S. (PI); Weissman, I. (PI); Wenstrand, J. (PI); White, R. (PI); Widom, J. (PI); Widrow, B. (PI); Wiederhold, G. (PI); Wong, P. (PI); Wong, S. (PI); Wooley, B. (PI); Xing, L. (PI); Yamamoto, Y. (PI); Zebker, H. (PI)

CS 1C: Introduction to Computing at Stanford

For those with limited experience with computers or who want to learn more about Stanford's computing environment. Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by the Resident Computer Consultant (RCC). Final project. Not a programming course.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Smith, S. (PI)

CS 1U: Practical Unix

A practical introduction to using the Unix operating system with a focus on Linux command line skills. Class will consist of video tutorials and weekly hands-on lab sections. The time listed on AXESS is for the first week's logistical meeting only. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Topics may be added, given sufficient interest. Course website: http://cs1u.stanford.edu
Terms: Aut, Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: King, S. (PI); Topalovic, E. (PI); Zelenski, J. (PI)

CS 2C: Multimedia Production

Sound, image and video editing techniques and applications, including understanding file formats and publishing multimedia online. Topics: GarageBand, Photoshop, iMovie, Final Cut Pro, and iDVD. Weekly lecture followed by lab section. Second unit for additional creative production assignments completed outside of class time and Final Project with group. Not a programming course, but will use computer multimedia applications heavily for editing.
Terms: Aut, Win | Units: 1-2 | Grading: Satisfactory/No Credit
Instructors: Scott, E. (PI)

CS 21N: Can Machines Know? Can Machines Feel?

Preference to freshmen. Can mental attitudes attributed to people and sometimes to animals, including knowledge, belief, desire, and intention, also be ascribed to machines? Can light sensors have a belief? Can a pool cleaning robot or tax-preparation software have an intention? If not, why not? If yes, what are the rules of such ascription, and do they vary between human beings and machines? Sources include philosophy, neuroscience, computer science, and artificial intelligence. Topics: logic, probability theory, and elements of computation. Students present a paper.
Terms: Aut | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Shoham, Y. (PI)

CS 47N: Computers and the Open Society

How online technologies change our lives and the social structure that we live in. Course emphasizes critical analyses of current trends i.e. blogging, social networks, and instant mobile communication. Readings include case studies and analyses of basic principles i.e. privacy, equity and sustainability. Guest speakers who have participated in development of computers and the net will share their experiences and enter into debates on current issues. Students work individually and in small groups to research issues, develop the capacity for critical thinking about them, and use the results as the basis for writing and discussions both in class and on-line.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Winograd, T. (PI)

CS 73N: The Business of the Internet

Preference to freshmen. Issues in Internet history, technology, and public policy are discussed as well as the Internet's impact on commerce, education, government, and health care. Writing for the web. Participants develop a substantial website.
Terms: Spr | Units: 3 | UG Reqs: Writing2, GER:DBEngrAppSci | Grading: Letter (ABCD/NP)

CS 75N: Cell Phones, Sensors, and You

Focuses on the role of cell phones as the first prevalent wearable sensors that gather information about you that can be both useful and potentially harmful. Topics include the state of technology, sociological and privacy implications, potential governmental regulation, etc. Addresses omniscient "big brother" technology including radar guns and the recording devices that led to the Watergate scandal. Students will gather and compile information on topics and come to class ready to discuss and debate with formulated opinions.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 76N: Elections and Technology

Freshmen Seminar. Since the disastrous Presidential election in Florida in 2000, problems with and worries about technology in elections have gained increasing attention. Are electronic voting machines secure? Are paper ballots secure? Why can't we just vote over our cell phones or the internet? Should voters have to show identification? How do legislators decide these things? How can technologists be heard? We'll look into these questions as we watch others struggle with them in the 2012 Presidential election.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Dill, D. (PI)

CS 77: Interaction Design Basics

Reduced version of CS 147, focusing on interaction, not implementation. As an introduction to the methods and principles of designing user interfaces, the course will cover topics such as needfinding, rapid prototyping, visual design, and interface evaluation. In addition to weekly lectures and quizzes, assignments culminate in a final design project consisting of an interactive prototype of a web application. Prerequisites: none.
Terms: Aut | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Klemmer, S. (PI)

CS 101: Introduction to Computing Principles

Introduces the essential ideas of computing: data representation, algorithms, programming "code", computer hardware, networking, security, and social issues. Students learn how computers work and what they can do through hands-on exercises. In particular, students will see the capabilities and weaknesses of computer systems so they are not mysterious or intimidating. Course features many small programming exercises, although no prior programming experience is assumed or required. CS101 is not a complete programming course such as CS106A. CS101 is effectively an alternative to CS105. A laptop computer is required for the in-class exercises. Limited enrollment
Terms: Spr | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Parlante, N. (PI)

CS 103: Mathematical Foundations of Computing

Mathematical foundations required for computer science, including propositional predicate logic, induction, sets, functions, and relations. Formal language theory, including regular expressions, grammars, finite automata, Turing machines, and NP-completeness. Mathematical rigor, proof techniques, and applications. May not be taken by students who have completed 103A,B or 103X. Prerequisite: 106A or equivalent.
Terms: Aut, Win, Spr | Units: 3-5 | UG Reqs: GER:DBMath | Grading: Letter or Credit/No Credit

CS 105: Introduction to Computers

For non-technical majors. What computers are and how they work. Practical experience in programming. Construction of computer programs and basic design techniques. A survey of Internet technology and the basics of computer hardware. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. Students with prior computer science experience at the level of 106 or above require consent of instructor. Prerequisite: minimal math skills.
Terms: Aut, Win | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 106A: Programming Methodology (ENGR 70A)

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the Java programming language. Emphasis is on good programming style and the built-in facilities of the Java language. No prior programming experience required.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 106B: Programming Abstractions (ENGR 70B)

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 106X: Programming Abstractions (Accelerated) (ENGR 70X)

Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Additional advanced material and more challenging projects. Prerequisite: excellence in 106A or equivalent, or consent of instructor.
Terms: Aut | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Cain, G. (PI)

CS 107: Computer Organization and Systems

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization. Prerequisites: 106B or X, or consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 108: Object-Oriented Systems Design

Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams. Prerequisite: 107.
Terms: Aut, Win, Sum | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Young, P. (PI)

CS 109: Introduction to Probability for Computer Scientists

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, MATH 51 or equivalent.
Terms: Win, Spr | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Sahami, M. (PI)

CS 109L: Statistical Computing with R Laboratory

Supplemental lab to CS109. Introduces the R programming language for statistical computing. Topics include basic facilities of R including mathematical, graphical, and probability functions, building simulations, introductory data fitting and machine learning. Provides exposure to the functional programming paradigm. Corequisite: CS109.
Terms: Win, Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Holtz, B. (PI); Sahami, M. (PI)

CS 110: Principles of Computer Systems

Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; security, and encryption; and performance optimizations. Prerequisite: 107.
Terms: Aut, Win, Spr | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

Automated processing of less structured information: human language text and speech, web pages, social networks, genome sequences, with goal of automatically extracting meaning and structure. Methods include: string algorithms, automata and transducers, hidden Markov models, graph algorithms, XML processing. Applications such as information retrieval, text classification, social network models, machine translation, genomic sequence alignment, word meaning extraction, and speech recognition. Prerequisite: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Manning, C. (PI)

CS 140: Operating Systems and Systems Programming

Operating systems design and implementation. Basic structure; synchronization and communication mechanisms; implementation of processes, process management, scheduling, and protection; memory organization and management, including virtual memory; I/O device management, secondary storage, and file systems. Prerequisite: CS 110.
Terms: Aut, Win | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 142: Web Applications

Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, javascript, and the document object model. Server-side technologies such as sessions, templates, relational databases, and object-relational mapping. Issues in web security and application scalability. New models of web application deployment. Prerequisites: CS 107 and CS 108.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ousterhout, J. (PI)

CS 143: Compilers

Principles and practices for design and implementation of compilers and interpreters. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Students construct a compiler for a simple object-oriented language during course programming projects. Prerequisites: 103 or 103B, and 107.
Terms: Spr, Sum | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Aiken, A. (PI)

CS 144: Introduction to Computer Networking

Principles and practice. Structure and components of computer networks, packet switching, layered architectures. Applications: web/http, voice-over-IP, p2p file sharing and socket programming. Reliable transport: TCP/IP, reliable transfer, flow control, and congestion control. The network layer: names and addresses, routing. Local area networks: ethernet and switches. Wireless networks and network security. Prerequisite: CS 110.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 145: Introduction to Databases

Database design and use of database management systems for applications. The relational model, relational algebra, and SQL, the standard language for creating, querying, and modifying relational databases. XML data including DTDs and XML Schema for validation, and the query and transformation languages XPath, XQuery and XSLT. UML database design, and relational design principles based on functional dependencies and normal forms. Indexes, views, transactions, authorization, integrity constraints, and triggers, and on-line analytical processing (OLAP). Guest speakers from industry and additional advanced topics as time and class interest permits. Prerequisites: 103 and 107 (or equivalent).
Terms: Aut, Sum | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Widom, J. (PI)

CS 147: Introduction to Human-Computer Interaction Design

Introduces fundamental methods and principles for designing, implementing, and evaluating user interfaces. Topics: user-centered design, rapid prototyping, experimentation, direct manipulation, cognitive principles, visual design, social software, software tools. Learn by doing: work with a team on a quarter-long design project, supported by lectures, readings, and studios. Prerequisite: 106B or X or equivalent programming experience.
Terms: Aut | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Klemmer, S. (PI)

CS 148: Introduction to Computer Graphics and Imaging

Topics: Image input and output devices such as cameras and displays, graphics hardware and software, input technologies and interactive techniques, typography and page layout, light and color representations, exposure and tone reproduction, image composition and imaging models, digital signal processing, sampling, aliasing and antialiasing, compression, two- and three-dimensional geometry and formations, modeling techniques including curves and surfaces, reflection models and illumination algorithms, and basic methods of animation. Progamming asssignments using C++ and OpenGL. Prerequisites: CS 107, MATH 51.
Terms: Aut, Sum | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Fedkiw, R. (PI)

CS 149: Parallel Computing

This course is an introduction to parallelism and parallel programming. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. Topics: varieties of parallelism in current hardware (e.g., fast networks, multicore, accelerators such as GPUs, vector instruction sets), importance of locality, implicit vs. explicit parallelism, shared vs. non-shared memory, synchronization mechanisms (locking, atomicity, transactions, barriers), and parallel programming models (threads, data parallel/streaming, futures, SPMD, message passing, SIMT, transactions, and nested parallelism). Significant parallel programming assignments will be given as homework. The course is open to students who have completed the introductory CS course sequence through 110 and have taken CS 143.
Terms: Win | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 154: Introduction to Automata and Complexity Theory

Regular sets: finite automata, regular expressions, equivalences among notations, methods of proving a language not to be regular. Context-free languages: grammars, pushdown automata, normal forms for grammars, proving languages non-context-free. Turing machines: equivalent forms, undecidability. Nondeterministic Turing machines: properties, the class NP, complete problems for NP, Cook's theorem, reducibilities among problems. Prerequisites: 103 or 103B.
Terms: Win, Sum | Units: 3-4 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Williams, R. (PI)

CS 155: Computer and Network Security

For seniors and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system holes, application security (web, email, databases), viruses, social engineering attacks, privacy, and digital rights management. Course projects focus on building reliable code. Prerequisite: 140. Recommended: basic Unix.
Terms: Spr | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 157: Logic and Automated Reasoning

An elementary exposition from a computational point of view of propositional and predicate logic, axiomatic theories, and theories with equality and induction. Interpretations, models, validity, proof, strategies, and applications. Automated deduction: polarity, skolemization, unification, resolution, equality. Prerequisite: 103 or 103B.
Terms: Aut | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Genesereth, M. (PI)

CS 161: Design and Analysis of Algorithms

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.
Terms: Aut, Spr, Sum | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 164: Computing with Physical Objects: Algorithms for Shape and Motion

Algorithms and data structures dealing with the representation and manipulation of physical objects and entities in the computer. Computational structures for shape and motion, shape fitting and matching, triangulations and other spatial subdivisions, and low-dimensional search and optimization. Examples relevant to computer graphics, computer vision, robotics and geometric computation emphasizing algorithmic paradigms applicable to multidimensional data. Prerequisites: CS 103 or 103B, and CS 109 or STATS 116, and CS 106B/X or consent of instructor.
Terms: not given this year | Units: 3 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit

CS 170: Stanford Laptop Orchestra: Composition, Coding, and Performance (MUSIC 128)

Classroom instantiation of the Stanford Laptop Orchestra (SLOrk) which includes public performances. An ensemble of more than 20 humans, laptops, controllers, and special speaker arrays designed to provide each computer-mediated instrument with its sonic identity and presence. Topics and activities include issues of composing for laptop orchestras, instrument design, sound synthesis, programming, and live performance. May be repeated four times for credit.
Terms: Spr | Units: 1-5 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Oh, J. (PI)

CS 173: A Computational Tour of the Human Genome

(Only one of 173 or 273A counts toward any CS degree program.) Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing; functional landscape of the human genome (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures on current genomic research topics. Class will be similar in spirit to CS273A, which will not be offered this year. Prerequisites: CS107 or equivalent background in programming.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bejerano, G. (PI)

CS 178: Digital Photography

Scientific, artistic, and computing aspects of digital photography. Topics: lenses and optics, light and sensors, optical effects in nature, perspective and depth of field, sampling and noise, the camera as a computing platform, image processing and editing, history of photography, computational photography. Counts as a CS elective in the Graphics track. Prerequisites: introductory calculus; students must have a digital camera with manual control over shutter speed and aperture. Loaner cameras may be available. No programming experience required. GER:DB-EngrAppSci
Terms: Spr | Units: 3-5 | UG Reqs: GER:DBEngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Levoy, M. (PI)

CS 181: Computers, Ethics, and Public Policy

(Formerly 201.) Primarily for majors entering computer-related fields. Ethical and social issues related to the development and use of computer technology. Ethical theory, and social, political, and legal considerations. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. Prerequisite: 106B or X.
Terms: Aut, Spr | Units: 4 | UG Reqs: GER:ECEthicReas | Grading: Letter or Credit/No Credit
Instructors: Cooper, S. (PI)

CS 181W: Computers, Ethics and Public Policy (WIM)

Writing-intensive version of CS181. Satisfies the WIM requirement for Computer Science and Computer Systems Engineering undergraduates.
Terms: Aut, Spr | Units: 4 | UG Reqs: GER:ECEthicReas | Grading: Letter (ABCD/NP)
Instructors: Cooper, S. (PI)

CS 191: Senior Project

Restricted to Computer Science and Computer Systems Engineering students. Group or individual projects under faculty direction. Register using instructor's section number. A project can be either a significant software application or publishable research. Software application projects include substantial programming and modern user-interface technologies and are comparable in scale to shareware programs or commercial applications. Research projects may result in a paper publishable in an academic journal or presentable at a conference. Required public presentation of final application or research results.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 191W: Writing Intensive Senior Project

Restricted to Computer Science and Computer Systems Engineering students. Writing-intensive version of CS191. Register using the section number of an Academic Council member.
Terms: Aut, Win, Spr | Units: 3-6 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pea, R. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 192: Programming Service Project

Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value.
Terms: Aut, Win, Spr, Sum | Units: 1-4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 193C: Client-Side Internet Technologies

Client-side technologies used to create web sites such as sophisticated Web 2.0 interfaces similar to Google maps. XHTML, CSS, JavaScript, document object model (DOM), AJAX, and Flash. Prerequisite: programming experience at the level of 106A.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit

CS 193P: iPhone and iPad Application Programming

Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, mobile device power management, multi-threading and performance considerations. Prerequisites: C language and object-oriented programming experience at 106B or X level. Recommended: CS107, UNIX, graphics, databases.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hegarty, P. (PI)

CS 194: Software Project

Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes a detailed proposal. Public demonstration of the project at the end of the quarter. Prerequisites: CS 110 and CS 161.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Young, P. (PI)

CS 194W: Software Project (WIM)

Restricted to Computer Science, Computer Systems Engineering, and Electrical Engineering undergraduates. Writing-intensive version of CS194.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Young, P. (PI)

CS 196: Computer Consulting

Focus is on Macintosh and Windows operating system maintenance and troubleshooting through hardware and software foundation and concepts. Topics include operating systems, networking, security, troubleshooting methodology with emphasis on Stanford's computing environment. Not a programming course. Prerequisite: 1C or equivalent.
Terms: Win, Spr | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Smith, S. (PI)

CS 198: Teaching Computer Science

Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Focus is on teaching skills, techniques, and course specifics. Application and interview required; see http://cs198.stanford.edu.
Terms: Aut, Win, Spr | Units: 3-4 | Grading: Satisfactory/No Credit

CS 199: Independent Work

Special study under faculty direction, usually leading to a written report. Letter grade; if not appropriate, enroll in 199P.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pea, R. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 199P: Independent Work

(Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Altman, R. (PI); Baker, M. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 202: Law for Computer Science Professionals

Intellectual property law as it relates to computer science including copyright registration, patents, and trade secrets; contract issues such as non-disclosure/non-compete agreements, license agreements, and works-made-for-hire; dispute resolution; and principles of business formation and ownership. Emphasis is on topics of current interest such as open source and the free software movement, peer-to-peer sharing, encryption, data mining, and spam.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Hansen, D. (PI)

CS 204: Computational Law

Legal informatics based on representation of regulations in computable form. Encoding regulations facilitate creation of legal information systems with significant practical value. Convergence of technological trends, growth of the Internet, advent of semantic web technology, and progress in computational logic make computational law prospects better. Topics: current state of computational law, prospects and problems, philosophical and legal implications. Prerequisite: basic concepts of programming.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 205A: Mathematical Methods for Robotics, Vision, and Graphics

Continuous mathematics background necessary for research in robotics, vision, and graphics. Possible topics: linear algebra; the conjugate gradient method; ordinary and partial differential equations; vector and tensor calculus. Prerequisites: 106B or X; MATH 51 and 113; or equivalents.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Fedkiw, R. (PI)

CS 205B: Mathematical Methods for Fluids, Solids, and Interfaces

Numerical methods for simulation of problems involving solid mechanics and fluid dynamics. Focus is on practical tools needed for simulation, and continuous mathematics involving nonlinear hyperbolic partial differential equations. Possible topics: finite element method, highly deformable elastic bodies, plasticity, fracture, level set method, Burgers' equation, compressible and incompressible Navier-Stokes equations, smoke, water, fire, and solid-fluid coupling. Prerequisite: 205A or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 207: The Economics of Software

How software products are moved into the marketplace and how the resulting intellectual property is exploited. Concepts that are outside of the common knowledge of computer scientists such as business terms and spreadsheet computations to quantitatively compare alternatives. Goal is to contribute to informed decision making in high-tech product design, acquisition, production, mar-keting, selection of business structures, outsourcing, and impact of taxation policies. No specific background required.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Wiederhold, G. (PI)

CS 208: Canon of Computer Science

Analysis and discussion of seminal works in computer science. Emphasis on works that changed the course of computing and continue to this day to provoke and stimulate. Course will study foundational ideas that are at the core of personal computing, artificial intelligence, computer systems, computer networks, and more. Through immersion in original literature, we can more deeply comprehend the present state of computing, its origins, its underlying assumptions, and its major open questions. In connecting students with the ideas that shaped computer science, course aims to instill lasting inspiration and a deep understanding of major trends in the field.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 210A: Software Project Experience with Corporate Partners

Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: 109 or 110.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Borenstein, J. (PI)

CS 210B: Software Project Experience with Corporate Partners

Continuation of CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: 109 or 210A.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Borenstein, J. (PI)

CS 221: Artificial Intelligence: Principles and Techniques

(Only one of 121 or 221 counts toward any CS degree program.) Topics: search, constraint satisfaction, knowledge representation, probabilistic models, Bayesian networks, machine learning, neural networks, vision, robotics, and natural language processing. Prerequisites: 103 or 103B/X; 106B or 106X; and exposure to probability. Recommended: 107 and facility with basic differential calculus.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Liang, P. (PI)

CS 222: Rational Agency and Intelligent Interaction (PHIL 358)

For advanced undergraduates, and M.S. and beginning Ph.D. students. Logic-based methods for knowledge representation, information change, and games in artificial intelligence and philosophy. Topics: knowledge, certainty, and belief; time and action; belief dynamics; preference and social choice; games; and desire and intention. Prerequisite: propositional and first-order logic.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 223A: Introduction to Robotics (ME 320)

Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khatib, O. (PI)

CS 224M: Multi-Agent Systems

For advanced undergraduates, and M.S. and beginning Ph.D. students. Topics: logics of knowledge and belief, other logics of mental state, theories of belief change, multi-agent probabilities, essentials of game theory, social choice and mechanism design, multi-agent learning, communication. Applications discussed as appropriate; emphasis is on conceptual matters and theoretical foundations. Prerequisites: basic probability theory and first-order logic.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Shoham, Y. (PI)

CS 224N: Natural Language Processing (LINGUIST 284)

Methods for processing human language information and the underlying computational properties of natural languages. Syntactic and semantic processing from linguistic and algorithmic perspectives. Focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, translation, and interpretation; and representative systems. Prerequisites: CS124 or CS121/221.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Manning, C. (PI)

CS 224S: Speech Recognition and Synthesis (LINGUIST 285)

Automatic speech recognition, speech synthesis, and dialogue systems. Focus is on key algorithms including noisy channel model, hidden Markov models (HMMs), Viterbi decoding, N-gram language modeling, unit selection synthesis, and roles of linguistic knowledge. Prerequisite: programming experience. Recommended: CS 221 or 229.
Terms: not given this year | Units: 2-4 | Grading: Letter or Credit/No Credit

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

Machine understanding of human language. Computational semantics (determination of word sense and synonymy, event structure and thematic roles, time, aspect, causation, compositional semantics, scopal operators), and computational pragmatics and discourse (coherence, coreference resolution, information packaging, dialogue structure). Theoretical issues, online resources, and relevance to applications including question answering and summarization. Prerequisites: one of LINGUIST 180 / CS 124 / CS 224N,S: and logic such as LINGUIST 130A or B, CS 157, or PHIL150).
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 224W: Social and Information Network Analysis

(Formerly 322) How do rumors and information spread? Who are the influencers? Can we predict friendships on Facebook? Networks are the core of the WWW, blogs, Twitter and Facebook. They can be characterized by the complex interplay between information content, millions of individuals and organizations that create it, and the technology that supports it. Course will focus on how to analyze the structure and dynamics of large networks, how to model links, and how design algorithms that work with such large networks. Topics: statistical properties of large networks, models of social network structure and evolution, link prediction, network community detection, diffusion of innovation, information propagation, six-degrees of separation, finding influential nodes in networks, disease outbreak detection, networks with positive and negative ties, and connections with work in the social sciences and economics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Leskovec, J. (PI)

CS 225A: Experimental Robotics

Hands-on laboratory course experience in robotic manipulation. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone. Prerequisites: 223A or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khatib, O. (PI)

CS 225B: Robot Programming Laboratory

For robotics and non-robotics students. Students program mobile robots to exhibit increasingly complex behavior (simple dead reckoning and reactivity, goal-directed motion, localization, complex tasks). Topics: motor control and sensor characteristics; sensor fusion, model construction, and robust estimation; control regimes (subsumption, potential fields); probabalistic methods, including Markov localization and particle filters. Student programmed robot contest. Programming is in C++ on Unix machines, done in teams. Prerequisite: programming at the level of 106B, 106X, 205, or equivalent.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 226: Statistical Techniques in Robotics

Theory and practice of statistical techniques used in robotics and large-scale sensor-based systems. Probabilistic state estimation, Bayes, Kalman, information and particle filters. Simultaneous localization and mapping techniques, and multi-robot sensor fusion. Markov techniques for making decisions under uncertainty, and probabilistic control algorithms and exploration.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 227: Knowledge Representation and Reasoning

Representing knowledge symbolically in a form suitable for automated reasoning, and associated reasoning methods. Combines formal algorithmic analysis with a description of recent applications. Topics: object-oriented knowledge representation, description logics, inheritance networks, logic programming, propositional satisfiability, contraint satisfaction, planning and scheduling, abductive explanation, tractable reasoning. Prerequisites: familiarity with basic notions in data structures and with techniques in algorithm design and analysis. Computational logic (CS157 or equivalent). Recommended: previous or concurrent course in AI. Knowledge of Lisp or Prolog programming.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 227B: General Game Playing

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Genesereth, M. (PI)

CS 228: Probabilistic Graphical Models: Principles and Techniques

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Koller, D. (PI)

CS 229: Machine Learning

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Ng, A. (PI)

CS 229A: Applied Machine Learning

Covers algorithms that allow computers to learn from data. Emphasis on practical skills and methods for applying learning techniques and building practical AI/Learning systems. Course covers commonly used learning techniques (classification, regression, clustering, dimensionality reduction), specific applications (anomaly detection, recommender systems, search), as well as working with big data. Online, self-paced course. Enrollment limited. Consent of instructor required. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as Math 51.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 229T: Statistical Learning Theory (STATS 231)

(Same as STATS 231) For a given learning problem, what methods should be employed, and under what assumptions can we expect them to work? This course focuses on developing algorithms for various scenarios (e.g., high-dimensional, online, unsupervised) as well as theoretical analyses of these algorithms. Topics include kernel methods, generalization bounds, spectral methods, online learning, and nonparametric Bayes. Prerequisites: A solid background in linear algebra and probability theory. Basic exposure to statistics and machine learning (STAT 315A or CS 229), and graphical models (CS 228) is helpful but not essential.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Liang, P. (PI)

CS 231A: Introduction to Computer Vision

(Formerly 223B) An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Li, F. (PI)

CS 231B: The Cutting Edge of Computer Vision

(Formerly 223C) More than one-third of the brain is engaged in visual processing, the most sophisticated human sensory system. Yet visual recognition technology has fundamentally influenced our lives on the same scale and scope as text-based technology has, thanks to Google, Twitter, Facebook, etc. This course is designed for those students who are interested in cutting edge computer vision research, and/or are aspiring to be an entrepreneur using vision technology. Course will guide students through the design and implementation of three core vision technologies: segmentation, detection and classification on three highly practical, real-world problems. Course will focus on teaching the fundamental theory, detailed algorithms, practical engineering insights, and guide them to develop state-of-the-art systems evaluated based on the most modern and standard benchmark datasets. Prerequisites: CS2223B or equivalent and a good machine learning background (i.e. CS221, CS228, CS229). Fluency in Matlab and C/C++.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Li, F. (PI)

CS 232: Digital Image Processing (EE 368)

Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, wavelets, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students write and investigate image processing algorithms in Matlab. Term project. Prerequisites: EE261, EE278B.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Girod, B. (PI)

CS 235: Applied Robot Design for Non-Robot-Designers: How to Fix, Modify, Design, and Build

Students will learn how to design and build the mechanical hardware of robots. The goal is to take people with no mechanical experience and teach them to build professional-quality robots. The course will consist of weekly labs and a final project, each of which will entail building an interesting robotic device. For example, students will build a pantilt camera turret in the belts lab. Topics will include: Electric motors, unusual actuators, sensors, mechanical transmissions, rotary and linear motion, counterbalancing, and standard mechanisms. Required graduate or PhD status; undergraduate students may enroll with instructor's permission.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 240: Advanced Topics in Operating Systems

Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. Prerequisite: 140 or equivalent.
Terms: Win, Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 241: Secure Web Programming

Building secure Web applications is key to the continued success of the Web. Course will cover the key components and available tools for securing web applications. Discussions on browser security policy and how to properly use it, server-side abstractions for building secure applications, and commong errors found in existing applications. Course will include student presentations on course projects.
Terms: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit

CS 242: Programming Languages

Central concepts in modern programming languages, impact on software development, language design trade-offs, and implementation considerations. Functional, imperative, and object-oriented paradigms. Formal semantic methods and program analysis. Modern type systems, higher order functions and closures, exceptions and continuations. Modularity, object-oriented languages, and concurrency. Runtime support for language features, interoperability, and security issues. Prerequisite: 107, or experience with Lisp, C, and an object-oriented language.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cooper, S. (PI)

CS 243: Program Analysis and Optimizations

Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. The methodology of applying mathematical abstractions such as graphs, fixpoint computations, binary decision diagrams in writing complex software, using compilers as an example. Topics include data flow analysis, instruction scheduling, register allocation, parallelism, data locality, interprocedural analysis, and garbage collection. Prerequisites: 103 or 103B, and 107.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Liao, S. (PI); Ullman, J. (PI); Whaley, J. (PI)

CS 244: Advanced Topics in Networking

Classic papers, new ideas, and research papers in networking. Architectural principles: naming, addressing, routing; congestion control, traffic management, QoS; wireless and mobility; overlay networks and virtualization; network security; switching and routing; content distribution; and proposals for future Internet structures. Prerequisite: 144 or equivalent.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Katti, S. (PI)

CS 244B: Distributed Systems

Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. Prerequisites: CS 144 and CS 249A.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cheriton, D. (PI)

CS 244C: Readings and Projects in Distributed Systems

Companion project option for 244B. Corequisite: 244B.
Terms: not given this year | Units: 3-6 | Grading: Letter or Credit/No Credit

CS 244E: Networked Wireless Systems (EE 384E)

Design and implementation of wireless networks and mobile systems. The course will commence with a short retrospective of wireless communication and initially touch on some of the fundamental physical layer properties of various wireless communication technologies. The focus will then shift to design of media access control and routing layers for various wireless systems. The course will also examine adaptations necessary at transport and higher layers to cope with node mobility and error-prone nature of the wireless medium. Finally, it will conclude with a brief overview of other related issues including emerging wireless/mobile applications. Prerequisites: EE 284
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 245: Database Systems Principles

File organization and access, buffer management, performance analysis, and storage management. Database system architecture, query optimization, transaction management, recovery, concurrency control. Reliability, protection, and integrity. Design and management issues. Prerequisites: 145, 161.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Garcia-Molina, H. (PI)

CS 246: Mining Massive Data Sets

Distributed file systems: Hadoop, map-reduce; PageRank, topic-sensitive PageRank, spam detection, hubs-and-authorities; similarity search; shingling, minhashing, random hyperplanes, locality-sensitive hashing; analysis of social-network graphs; association rules; dimensionality reduction: UV, SVD, and CUR decompositions; algorithms for very-large-scale mining: clustering, nearest-neighbor search, gradient descent, support-vector machines, classification, and regression; submodular function optimization. Prerequisites: At lease one of CS107 or CS145; at least one of CS109 or STAT116, or equivalent.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Leskovec, J. (PI)

CS 247: Human-Computer Interaction Design Studio

Project-based. Methods used in interaction design including needs analysis, user observation, idea sketching, concept generation, scenario building, storyboards, user character stereotypes, usability analysis, and market strategies. Prerequisites: 147 and 106A or equivalent background in programming.
Terms: Win | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Bernstein, M. (PI); Heer, J. (PI)

CS 247L: Human Computer Interaction Technology Laboratory

Hands-on introduction to contemporary HCI technologies. Interaction design with Adobe Flash, mobile development, physical computing, and web applications. Corequisite: 247.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Heer, J. (PI)

CS 248: Interactive Computer Graphics

Rendering and animation for interactive computer graphics. Topics in rendering include: the graphics pipeline, rasterization, lighting and surface shading, texture mapping and its applications, graphics hardware, and rendering optimization. Topics in animation include: keyframing and interpolation, physics-based simulation, and character animation. Prerequisite: CS148.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Lentine, M. (PI); Su, J. (PI)

CS 249A: Object-Oriented Programming from a Modeling and Simulation Perspective

Topics: large-scale software development approaches for complex applications, class libraries and frameworks; encapsulation, use of inheritance and dynamic dispatch, design of interfaces and interface/implementation separation, exception handling, smart pointers and reference management, minimalizing dependencies and value-oriented programming. Inheritance: when and why multiple inheritance naming, directories, manager, and disciplined use of design patterns including functors, event notification and iterators. Prerequisites: C, C++, and programming methodology as developed in 106B or X, and 107 (107 may be taken concurrently). Recommended: 193D.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cheriton, D. (PI)

CS 249B: Large-scale Software Development

Software engineering of high quality large-scale complex software with a focus on evolvability, performance and cost. Software development processes, people and practice; audit: integrating invariant checks with production software; concurrency with modular object-oriented programming; collection implementation; generic programming and templates; design of value types; named descriptions for large value types; memory management; controlling placement, locality and consumption; run-time vs. static type checking and identification.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 254: Computational Complexity

An introduction to computational complexity theory. The P versus NP problem; diagonalization and relativization; space complexity, Savitch's algorithm, NL=coNL, Reingold's algorithm; counting problem and #P-completeness; circuit complexity; pseudorandomness, derandomixation, and the Natural Proofs barrier; complexity of approximation; quantum computing. Prerequisites: 154 or equivalent; mathematical maturity.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 255: Introduction to Cryptography

For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (single and double key), digital signatures, pseudo-random bit generation, authentication, electronic commerce (anonymous cash, micropayments), key management, PKI, zero-knowledge protocols. Prerequisite: basic probability theory.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boneh, D. (PI)

CS 259: Security Analysis of Network Protocols

General methods for security modeling and analysis, illustrated using network protocol security. Common security protocols and their properties including secrecy, authentication, key establishment, and fairness. Fully automated, finite-state, model-checking techniques. Constraint solving, process algebras, protocol logics, probabilistic model checking, and game theory. Students select a protocol, web component, hardware architecture, or other system to analyze, specify it in a chosen model, use an analysis tool or method to find vulnerabilities and verify properties, and present findings.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Franklin, J. (PI)

CS 259Q: Quantum Computing

The course introduces the basics of quantum algorithms, quantum computational complexity, quantum information theory, and quantum cryptography, including the models of quantum circuits and quantum Turing machines, Shor's factoring algorithms, Grover's search algorithm, the adiabatic algorithms, quantum error-correction, impossibility results for quantum algorithms, Bell's inequality, quantum information transmission, and quantum coin flipping. Prerequisites: knowledge of linear algebra, discrete probability and algorithms.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Trevisan, L. (PI)

CS 261: Optimization and Algorithmic Paradigms

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms. Prerequisite: 161 or equivalent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Plotkin, S. (PI)

CS 262: Computational Genomics (BIOMEDIN 262)

Applications of computer science to genomics, and concepts in genomics from a computer science point of view. Topics: dynamic programming, sequence alignments, hidden Markov models, Gibbs sampling, and probabilistic context-free grammars. Applications of these tools to sequence analysis: comparative genomics, DNA sequencing and assembly, genomic annotation of repeats, genes, and regulatory sequences, microarrays and gene expression, phylogeny and molecular evolution, and RNA structure. Prerequisites: 161 or familiarity with basic algorithmic concepts. Recommended: basic knowledge of genetics.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Batzoglou, S. (PI)

CS 266: Parameterized Algorithms and Complexity

An introduction to the area of parameterized algorithms and complexity, which explores multidimensional methods for measuring the difficulty and feasibility of solving computational problems. Topics include: fixed-parameter tractability (FPT) and its characterizations, FPT algorithms for hard problems, the W-hierarchy (W[1], W[2], W[P], and complete problems for these classes), and the relationships between parameterized questions and classical theory questions. Prerequisites: CS 154 and 161 or the equivalent mathematical maturity.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Williams, R. (PI)

CS 268: Geometric Algorithms

Techniques for design and analysis of efficient geometric algorithms for objects in 2-, 3-, and higher dimensions. Topics: convexity, triangulations and simplicial complexes, sweeping, partitioning, and point location. Voronoi/Delaunay diagrams and their properties. Arrangements of curves and surfaces. Intersection and visibility problems. Geometric searching and optimization. Random sampling methods. Impact of numerical issues in geometric computation. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, model-based recognition in computer vision, and structural molecular biology. Prerequisite: discrete algorithms at the level of 161. Recommended: 164.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Guibas, L. (PI)

CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)

Methods for modeling biomedical systems and for making those models explicit in the context of building software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Recommended: exposure to object-oriented systems, basic biology.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Musen, M. (PI); Gimenez, F. (TA); Mortensen, J. (TA); Planey, C. (TA)

CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)

Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Prerequisites: BIOMEDIN 210, 211, 214, 217 or consent of instructor.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

CS 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A)

Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing (technologies, assembly, personalized sequencing); functional landscape (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures from genomic researchers. No prerequisites. See http://cs273a.stanford.edu/.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)

Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, Gibbs Sampling, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisites: programming skills; consent of instructor for 3 units.
Terms: Aut | Units: 3-4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Altman, R. (PI)

CS 275: Translational Bioinformatics (BIOMEDIN 217)

Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of CS 106A and familiarity with statistics and biology.
Terms: Spr | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Butte, A. (PI)

CS 275A: Symbolic Musical Information (MUSIC 253)

Focus on symbolic data for music applications including advanced notation systems, optical music recognition, musical data conversion, and internal structure of MIDI files.
Terms: Win | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Sapp, C. (PI); Selfridge-Field, E. (PI)

CS 275B: Music Query, Analysis, and Style Simulation (MUSIC 254)

Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.
Terms: Spr | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Sapp, C. (PI); Selfridge-Field, E. (PI)

CS 276: Information Retrieval and Web Search (LINGUIST 286)

Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems. Text clustering and classification: classification algorithms, latent semantic indexing, taxonomy induction; Web search engines including crawling and indexing, link-based algorithms, and web metadata. Prerequisites: CS 107, CS 109, CS 161.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

CS 277: Experimental Haptics

Haptics as it relates to creating touch feedback in simulated or virtualized environments. Goal is to develop virtual reality haptic simulators and applications. Theoretical topics: psychophysical issues, performance and design of haptic interfaces, haptic rendering methods for 3-D virtual environments, and haptic simulation and rendering of rigid and deformable solids. Applied topics: the CHAI haptic library; implementation of haptic rendering algorithms; collision detection in 3-D environments; design of real-time models for deformable objects. Guest speakers. Lab/programming exercises; a more open-ended final project. Enrollment limited to 20. Prerequisite: experience with C++. Recommended: 148 or 248, 223A.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 279: Computational Methods for Analysis and Reconstruction of Biological Networks

Types of interactions, including: regulatory such as transcriptional, signaling, and chromatin modification; protein-protein interactions; and genetic. Biological network structure at scales such as single interaction, small subgraphs, and global organization. Methods for analyzing properties of biological networks. Techniques for reconstructing networks from biological data, including: DNA/protein sequence motifs and sequence conservation; gene expression data; and physical binding data such as protein-DNA, protein-RNA, and protein-protein. Network dynamics and evolution. Prerequisites: biology at the level of BIOSCI 41; computer science and data structures at the level of CS 103 and 106; and probability and statistics at the level of STATS 116 or CS 109.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 294: Research Project in Computer Science

Student teams work under faculty supervision on research and implementation of a large project in some major sub-discipline in computer science. Lectures on state-of-the-art methods related to the particular problem domain. Prerequisites: consent of instructor.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 294A: Research Project in Artificial Intelligence

Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 294H: Research Project in Human-Computer Interaction

Student teams under faculty supervision work on research and implementation of a large project in HCI. State-of-the-art methods related to the problem domain. Prerequisites CS 377, 147, 247, or permission from instructor.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 294S: Research Project in Software Systems and Security

Topics vary. Focus is on emerging research themes such as programmable open mobile Internet that spans multiple system topics such as human-computer interaction, programming systems, operating systems, networking, and security. May be repeated for credit. Prerequisites: CS 103 and 107.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Lam, M. (PI)

CS 295: Software Engineering

Software specification, testing, and verification. Emphasis is on current best practices and technology for developing reliable software at reasonable cost. Assignments focus on applying these techniques to realistic software systems. Prerequisites: 108. Recommended a project course such as 140, 143, or 145.
Terms: not given this year | Units: 2-3 | Grading: Letter or Credit/No Credit

CS 298: Seminar on Teaching Introductory Computer Science

Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Terms: not given this year | Units: 1-3 | Grading: Satisfactory/No Credit

CS 300: Departmental Lecture Series

Priority given to first-year Computer Science Ph.D. students. CS Masters students admitted if space is available. Presentations by members of the department faculty, each describing informally his or her current research interests and views of computer science as a whole.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Dill, D. (PI)

CS 303: Designing Computer Science Experiments

Introduction to empirical research in computer science. Learn how to design, execute, interpret, and report on computer science experiments. Conducting empirical work and using experiments to build theory is one of the major ways to move computer science forward, but these issues are often omitted from computer science curricula. Course features case studies drawn from artificial intelligence, systems, and human-computer interaction. Emphasizes the decision-making aspects of research and the logic behind research procedures.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 309: Industrial Lectureships in Computer Science

Guest computer scientist. By arrangement. May be repeated for credit. (Staff)
Terms: offered occasionally | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

CS 309A: Cloud Computing

For technology and business students. The shift from traditional software model of disconnected development and CD-ROM deployment to engineering and delivery on the Internet as a service. Guest industry experts are typically CEOs of public companies who are delivering applications, platform or compute and storage cloud based services.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Chou, T. (PI)

CS 315A: Parallel Computer Architecture and Programming

The principles and tradeoffs in the design of parallel architectures. Emphasis is on naming, latency, bandwidth, and synchronization in parallel machines. Case studies on shared memory, message passing, data flow, and data parallel machines illustrate techniques. Architectural studies and lectures on techniques for programming parallel computers. Programming assignments on one or more commercial multiprocessors. Prerequisites: EE 282, and reasonable programming experience.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 315B: Parallel Computing Research Project

Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. Research project. Prerequisite: consent of instructor.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 316: Advanced Multi-Core Systems (EE 382E)

In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Students will become familiar with complex trade-offs between performance-power-complexity and hardware-software interactions. A central part of CS316 is a project on an open research question on multi-core technologies. Prerequisites: EE 108B. Recommended: CS 149, EE 282.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 319: Topics in Digital Systems

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 321: Information Processing for Sensor Networks

Design and implementation of algorithms and protocols for performing information processing tasks in sensor networks, including routing, data dissemination and aggregation, information discovery and brokerage, service establishment (localization, time synchronization), sensor tasking and control, and distributed data storage. Techniques from signal processing, networking, energy-ware computing, distributed databases and algorithms, and embedded systems and platforms. Physical, networking, and application layers and design trade-offs across the layers. Prerequisites: linear algebra and elementary probability, networking background at the level of 144A or EE 284.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 324: Robot Perception

Advanced instruction and project work on robot perception, primarily focused on perception for manipulating objects, but this can include perception of people and other moving objects. Tools such as the Robot Operating System (ROS), the Open Source Computer Vision Library (Open CV), the Point Cloud Processing Library (PCL), and the Navigation, Planning, and Manipulation stacks on the PR2 robot. Review of the principles and code behind these tools so that the student has the basics to do state-of-the-art, publishable work in mobile robotic manipulation. Work is done on real robots. Limited enrollment. Recommended: CS 223A, CS 223B.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 326A: Motion Planning

Computing object motions in computer graphics, geometrical computing, robotics, or artificial intelligence for applications such as design, manufacturing, robotics, animated graphics, surgical planning, drug design, assembly planning, graphic animation of human figures, humanoid robots, inspection and surveillance, simulation of crowds, and biology. Path planning methods to generate collision-free paths among static obstacles. Extensions include uncertainty, mobile obstacles, manipulating moveable objects, maneuvering with kinematic constraints, and making and breaking contacts. Configuration space, geometric arrangements, and random sampling. Theoretical methods.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 327A: Advanced Robotic Manipulation

Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khatib, O. (PI)

CS 328: Topics in Computer Vision

Fundamental issues of, and mathematical models for, computer vision. Sample topics: camera calibration, texture, stereo, motion, shape representation, image retrieval, experimental techniques. May be repeated for credit. Prerequisites: 205, 223B, or equivalents.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 329: Topics in Artificial Intelligence

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 331: Advanced Reading in Computer Vision

(Formerly CS323) The field of computer vision has seen an explosive growth in past decade. Much of recent effort in vision research is towards developing algorithms that can perform high-level visual recognization tasks on real-world images and videos. With development of Internet, this task becomes particularly challenging and interesting given the heterogeneous data on the web. Course will focus on reading recent research papers that are focused on solving high-level visual recognition problems, such as object recognition and categorization, scene understanding, human motion understanding, etc. Project required. Prerequisite: some experience in research with one of the following fields: computer vision, image processing, computer graphics, machine learning.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Li, F. (PI)

CS 334A: Convex Optimization I (CME 364A, EE 364A)

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, EE178/278A.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boyd, S. (PI)

CS 340: Topics in Computer Systems

Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.
Terms: offered occasionally | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 340V: Networked Systems for Virtual Worlds

Open to graduate students and advanced undergraduates. Systems and networking aspects of building large, distributed virtual 3D environments, with a focus on scalability, consistency, security, fairness, and federation. Topics include existing architectures, naming, routing, caching, migration, interoperability, and attribution. Open-ended research project. Prerequisite: some systems and networking background. May be repeated for credit.
Terms: not given this year | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 341: Project in Mining Massive Data Sets

Team project in data-mining of very large-scale data, including the problem statement and implementation and evaluation of a solution; some lectures on relevant materials will be given: Hadoop, Hive, Amazon EC2; other topics of possible relevance to some projects: computational advertising and the adwords problem; graph partitioning and community detection; extracting relations from the Web; stream data processing.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Leskovec, J. (PI); Rajaraman, A. (PI); Ullman, J. (PI); Weigend, A. (PI)

CS 342: Programming Language Design

Tools for analysis and optimization of iterative coding systems. LDPC codes, Turbo codes, RA codes, optimized ensembles, message passing algorithms, density evolution, analytic techniques. Prerequisite: 376A.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 343: Advanced Topics in Compilers

Topics change every year. May be repeated for credit. Prerequisite: 243.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Engler, D. (PI)

CS 344: Topics in Computer Networks

High-performance embedded system design. Student teams of two software engineers (C experience required) and one hardward engineer (Verilog experience required) build a fully functioning Internet router Work in teams of three. How router interoperates with others in class. Open-ended design challenge judged by panel of industry experts. Prerequisites: CS 144, 244, or network programming experience.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 344B: Advanced Topics in Distributed Systems

Continuation of 244B. The use of distributed systems research in practical systems. New applications due to the growth in high-bandwidth connections. Distributed systems knowledge and techniques from research and system implementations, and active research topics. Readings include research publications.
Terms: not given this year | Units: 2 | Grading: Letter or Credit/No Credit

CS 344E: Advanced Wireless Networks

Networking research in wireless systems. Topics include: multi-channel/multi-radio systems, routing, coding, physical layer hints, low power, mesh networking, interference cancellation, technological trends, and protocol design. Students implement and test research ideas on SWAN, a WiFi testbed.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 345: Advanced Topics in Database Systems

Content varies. May be repeated for credit with instructor consent. Prerequisite: 145. Recommended: 245.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 347: Parallel and Distributed Data Management

The principles and system organization of distributed and parallel databases. Data fragmentation and distribution, distributed database design, query processing and optimization, distributed concurrency control, reliability and commit protocols, and replicated data management. Data management in peer-to-peer systems. Data management in the "cloud" using map-reduce and other massive parallelism techniques.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Garcia-Molina, H. (PI)

CS 348A: Computer Graphics: Geometric Modeling

The mathematical tools needed for the geometrical aspects of computer graphics and especially for modeling smooth shapes. Fundamentals: homogeneous coordinates, transformations, and perspective. Theory of parametric and implicit curve and surface models: polar forms, Bezier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Subdivision surfaces and multiresolution representations of geometry. Representations of solids and conversions among them. Surface reconstruction from scattered data points. Geometry processing on meshes, including simplification. Prerequisite: linear algebra. Recommended: 164, 248.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Guibas, L. (PI)

CS 348B: Computer Graphics: Image Synthesis Techniques

Intermediate level, emphasizing the sampling, shading, and display aspects of computer graphics. Topics: local and global illumination methods including radiosity and distributed ray tracing, texture generation and rendering, volume rendering, strategies for anti-aliasing and photo-realism, human vision and color science as they relate to computer displays, and high-performance architectures for graphics. Written assignments and programming projects. Prerequisite: 248 or equivalent. Recommended: Fourier analysis or digital signal processing.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Pharr, M. (PI)

CS 349: Topics in Programming Systems

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 349C: Topics in Programming Systems: Readings in Distributed Systems

Discussion of research publications that are of current interest in distributed systems. Students are expected to read all papers, and sign up for presentation of one paper. The course itself is 1 unit. Those interested in working on a project along with the readings should enroll for 3 units.
Terms: not given this year | Units: 1-3 | Grading: Letter or Credit/No Credit

CS 354: Topics in Circuit Complexity

An overview of circuit complexity, focusing on limitations of solving computational problems with circuits. Classical methods: diagonalization; the gate elimination method; the method of random restrictions; approximating circuits with polynomials. Connections between circuit-analysis algorithms and circuit complexity: learning circuits via queries; pseudorandomness and derandomization; satisfiability algorithms. Prerequisite: CS254 or the equivalent mathematical maturity.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 355: Advanced Topics in Cryptography

Topics: pseudo-random generation, zero knowledge protocols, elliptic curve systems, threshold cryptography, security analysis using random oracles, lower and upper bounds on factoring and discrete log. May be repeated for credit. Prerequisite: 255.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 357: Advanced Topics in Formal Methods

Topics vary annually. Possible topics include automata on infinite words, static analysis methods, runtime analysis methods, verification of real-time and hybrid systems, and formalization of middleware services. May be repeated for credit. Prerequisite: 256.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 358: Topics in Programming Language Theory

Topics of current research interest in the mathematical analysis of programming languages, structured operational semantics, domain theory, semantics of concurrency, rich type disciplines, problems of representation independence, and full abstraction. See Time Schedule or Axess for current topics. May be repeated for credit. Prerequisites: 154, 157, 258, or equivalents. (Staff)
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 359: Topics in the Theory of Computation

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 359G: Graph Partitioning and Expanders

Three topics related to the mathematics of expander graphs: (1) Approximation algorithms for finding a sparse balanced cut iin a graph (spectral partitioning, Leighton-Rao algorithm, and Arora-Rao-Vazirani algorithm; (2) Explicit construction of expander graphs (combinatorial and algebraic); and (3) Analysis of Markov-Chain Monte-Carlo algorithm via the estimation of the convergence of certain random walks. Recommended: a basic course in linear algebra and a course on algorithms.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 361A: Advanced Algorithms

Advanced data structures: union-find, self-adjusting data structures and amortized analysis, dynamic trees, Fibonacci heaps, universal hash function and sparse hash tables, persistent data structures. Advanced combinatorial algorithms: algebraic (matrix and polynomial) algorithms, number theoretic algorithms, group theoretic algorithms and graph isomorphism, online algorithms and competitive analysis, strings and pattern matching, heuristic and probabilistic analysis (TSP, satisfiability, cliques, colorings), local search algorithms. May be repeated for credit. Prerequisite: 161 or 261, or equivalent.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 364A: Algorithmic Game Theory

Topics at the interface of theoretical computer science and game theory such as: algorithmic mechanism design; combinatorial and competitive auctions; congestion and potential games; cost sharing; existence, computation, and learning of equilibria; game theory and the Internet; network games; price of anarchy; and selfish routing. Prerequisites: 154N and 161, or equivalents.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 364B: Topics in Algorithmic Game Theory

Topics on the interface of theoretical computer science and game theory. May be taken prior to 364A; may be repeated for credit. Prerequisites: 154N and 161, or equivalents.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 365: Randomized Algorithms (CME 309)

Design and analysis of algorithms that use randomness to guide their computations. Topics include: basic tools, from probability theory and probabilistic analysis that are recurrent in algorithmic applications; randomized complexity theory and game-theoretic techniques; algebraic techniques, probability amplification and derandomization. Applications: sorting and searching, data structures, combinatorial optimization and graph algorithms, geometric algorithms and linear programming, approximation and counting problems, similarity search and metric embeddings, online algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Goel, A. (PI)

CS 366: Graph Partitioning and Expanders

Three topics related to the mathematics of expander graphs: 1) Approximation algorithms for finding a sparse balanced cut in a graph (spectral partitioning, Leighton-Rao algorithm, and Arora-Rao-Vazirani algorithm); 2) Explicit construction of expander graphs (combinatorial and algebraic); and 3) Analysis of Markov-Chain Monte-Carlo algorithm via the estimation of the convergence of certain random walks. Prerequisites: Basic course in linear algebra and a course on algorithms, preferably; also a basic understanding of linear programming and of duality.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Trevisan, L. (PI)

CS 369: Topics in Analysis of Algorithms

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Plotkin, S. (PI)

CS 369N: Beyond Worst-Case Analysis

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 374: Algorithms in Biology (BIOMEDIN 374)

Algorithms and computational models applied to molecular biology and genetics. Topics vary annually. Possible topics include biological sequence comparison, annotation of genes and other functional elements, molecular evolution, genome rearrangements, microarrays and gene regulation, protein folding and classification, molecular docking, RNA secondary structure, DNA computing, and self-assembly. May be repeated for credit. Prerequisites: 161, 262 or 274, or BIOCHEM 218, or equivalents.
Terms: not given this year | Units: 2-3 | Grading: Letter or Credit/No Credit

CS 376: Research Topics in Human-Computer Interaction

Prepares students to conduct original HCI research by reading and discussing seminal and cutting-edge research papers. This broad introduction covers topics in design, social software, input techniques, mobile, and ubiquitous computing. Student pairs perform a quarter-long mini research project; students are encouraged to select topics that leverage larger research efforts on campus. For undergraduates, CS147 is a prerequisite.
Terms: Spr | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Bernstein, M. (PI)

CS 377: Topics in Human-Computer Interaction

Contents change each quarter. May be repeated for credit. See http://hci.stanford.edu/academics for offerings.
Terms: offered occasionally | Units: 2-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 377D: Topics in Learning and Technology: d.compress - Designing Calm (EDUC 328A)

Contents of the course change each year. The course can be repeated. Stress silently but steadily damages physical and emotional well-being, relationships, productivity, and our ability to learn and remember. This highly experiential and project-oriented class will focus on designing interactive technologies to enable calm states of cognition, emotion, and physiology for better human health, learning, creativity and productivity.
Terms: Win, Spr | Units: 2-3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Moraveji, N. (PI)

CS 377L: Learning in a Networked World: Learning Analytics in Technology-Enhanced Education (EDUC 298)

Foundations, theories and empirical studies for interdisciplinary advances in how we conceive of the potentials and challenges associated with lifelong, lifewide and life-deep learning in a networked world given the growth of always-on cyberinfrastructure for supporting information and social networks across space and time with personal computers, netbooks, and mobiles.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 377T: Behavior Design for Better Health

Design solutions to improve health behaviors. Discover activity sequences that create lasting habits. Use tech platforms to trigger & facilitate. Rapid iteration to improve designs. Project oriented but coding is not required.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Fogg, B. (PI)

CS 378: Phenomenological Foundations of Cognition, Language, and Computation

Critical analysis of theoretical foundations of the cognitive approach to language, thought, and computation. Contrasts of the rationalistic assumptions of current linguistics and artificial intelligence with alternatives from phenomenology, theoretical biology, critical literary theory, and socially-oriented speech act theory. Emphasis is on the relevance of theoretical orientation to the design, implementation, and impact of computer systems as it affects human-computer interaction.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 379: Interdisciplinary Topics

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 379C: Computational Models of the Neocortex

Reprisal of course offered spring 2012 of the same name ; see http://www.stanford.edu/class/cs379c/ for more detail ; which emphasized scaling the technologies of systems neuroscience to take advantage of the exponential trend in computational power known as Moore's Law. Course covers many of the same topics but will focus on the near-term prospects for practical advances in health care, prosthetic augmentation, and artificial intelligence inspired by biological systems. Graded pass / no credit on the basis of class participation, a midterm white paper or business prospectus and a final technical report evaluating an appropriate technology selected in collaboration with the instructor. Focus will be on examining the assumptions underlying current claims for realizing the potential benefits of research in neuroscience and identifying real business opportunities, disruptive new technologies and advances in medicine that could substantially benefit patients within the next decade. Technology-minded critical thinkers seriously interested in placing their bets and picking careers in related areas of business, technology and science are welcome. Prerequisites: basic probability theory, algorithms, and statistics.
Terms: Spr | Units: 3 | Grading: Satisfactory/No Credit
Instructors: Dean, T. (PI)

CS 390A: Curricular Practical Training

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. 390 A, B, and C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 390B: Curricular Practical Training

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. 390A,B,C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 390C: Curricular Practical Training

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. 390A,B,C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 393: Computer Laboratory

For CS graduate students. A substantial computer program is designed and implemented; written report required. Recommended as a preparation for dissertation research. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 395: Independent Database Project

For graduate students in Computer Science. Use of database management or file systems for a substantial application or implementation of components of database management system. Written analysis and evaluation required. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 399: Independent Project

Letter grade only.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pea, R. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 399P: Independent Project

Graded satisfactory/no credit.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 402: Beyond Bits and Atoms: Designing Technological Tools (EDUC 236X)

Practicum in designing and building technology-enabled curricula and learning environments. Students use software toolkits and state-of-the-art fabrication machines to design educational software, educational toolkits, and tangible user interfaces. How to design low-cost technologies, particularly for urban school in the US and abroad. The constructionist learning design perspective, critical pedagogy, and the application of complexity sciences in education.
Terms: Aut | Units: 3-5 | Grading: Letter or Credit/No Credit
Instructors: Blikstein, P. (PI); Stringer, D. (PI)

CS 402L: Beyond Bits and Atoms - Lab (EDUC 211X)

This course is a hands-on lab in the prototyping and fabrication of tangible technologies, with a special focus in learning and education. We will learn how to use state-of-the-art fabrication machines (3D printers, 3D scanners, laser cutters, routers) to design educational toolkits, educational toys, science kits, and tangible user interfaces. A special focus of the course will be to design low-cost technologies, particularly for urban school in the US and abroad.
Terms: Aut | Units: 1-3 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Blikstein, P. (PI)

CS 431: High-Level Vision: Object Representation (PSYCH 250)

(Formerly CS423 High-Level Vision: Behaviors, Neurons, and Computational Models) Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, Computer Science and Applied Statistics. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field. Topics include: theories of object perception, neural computations underlying invariant object perception, how visual exemplars and categories are represented in the brain, what information is present in distributed activations across neural populations and how it relates to object perception, what modern statistical and analytical tools there are for multi-variate analysis of brain activations.
Terms: not given this year | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 438X: Innovations in Education: Designing the teaching experience (EDUC 338X)

A hands-on class about teaching online. Each year students in this course explore a new design challenge related to teaching. Teaching online presents a unique set of challenges and opportunities. From amateur hobbyist to college professor, how might we give every individual the tools to be the best teacher they can be? We welcome graduate students from a wide range of disciples. Admission by application. Please see more information at http://dschool.stanford.edu.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Forssell, K. (PI)

CS 442: High Productivity and Performance with Domain-specific Languages in Scala

Introduction to developing domain specific languages (DSLs) for productivity and performance using the Scala programming language. Goal is to equip students with the knowledge and tools to develop DSLs that can dramatically improve the experience of using high performance computation in important scientific and engineering domains. Aimed at two sorts of students: domain experts who can define key domain specific language elements that capture domain knowledge, and computer scientists who can implement these DSLs using a new DSL framework in Scala. First half of the course will focus on understanding the infrastructure for implementing DSLs in Scala and developing techniques for defining good DSLs. Second half of the course will focus on example DSLs that provide both high-productivity and performance. During the second half of the course groups of students will develop and implement their own DSLs using the Delite DSL process of implementing DSLs for parallel computation. Prerequisites: Systems course such as CS140, CS143 or CS149, and expertise is a particular domain and desire to improve productivity and performance of computation.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Olukotun, O. (PI)

CS 447: Software Design Experiences

Small teams develop technology prototypes combining product and interaction design. Focus is on software and hardware interfaces, interaction, design aesthetics, and underpinnings of successful design including a reflective, interactive design process, group dynamics of interdisciplinary teamwork, and working with users. Prerequisite: CS 247A.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 448: Topics in Computer Graphics

Topic changes each quarter. Recent topics: computational photography, data visualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offererings and prerequisites. May be repeated for credit.
Terms: offered occasionally | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 448B: Data Visualization

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of 147, 148, or equivalent.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Heer, J. (PI)

CS 448M: Simulation of Human Movement

Foundations of human movement and motor control. Motion capture, motion graphs, and kinematic motion controllers. Physics-based optimization and dynamic controllers. Simulation of walking, running, jumping, balance, object manipulation, and airborne maneuvers. Prerequisites: CS248 and CS205A, or equivalent.
Terms: not given this year | Units: 1-4 | Grading: Letter or Credit/No Credit

CS 450: Introduction to Biotechnology

Academic and industrial experts discuss latest developments in fields such as bioenergy, green process technology, the production of industrial chemicals from renewable resources, protein pharmaceutical production, industrial enzyme production, stem cell applications, medical diagnostics, and medical imaging. Discussions of biotechnology ethics, business and patenting issues, and entrepreneurship in biotechnology.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

CS 468: Geometry Processing Algorithms

Contents of this course change with each offering. Past offerings have included geometric matching, surface reconstruction, collision detection, computational topology, etc. May be repeated for credit. Fall quarter 2010/11 topic will be Geometry Processing Algorithms. Techniques for modeling and efficient processing of polygonal geometric models. Topics: data structures for polygonal models, discrete differential geometry, mesh parameterization, mesh simplication and remeshing reconstruction from point clouds, mesh editing and deformation, geometric image editing. Recommended: 164.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 469: Algorithms in Mobile Applications

Review of algorithmic techniques used in popular mobile applications. Students will learn about uses of machine learning, information retrieval, and computer vision in mobile applications. Guest lectures are from industry experts who have created or led successful products. Prerequisites: CS 161.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Ramkumar, G. (PI)

CS 476B: Music, Computing, Design II: Mobile Music (MUSIC 256B)

Aesthetic, design, and implementation of mobile music, centered around the modern super smartphones such as the iPhone). Similarities and intrinsic differences between mobile and traditional computing and design for music. Topics include mobile software design, social and cloud computing, mobile interface design, and programming phones, in the service of music. Prerequisite: MUSIC 256A.
Terms: not given this year | Units: 1-4 | Grading: Letter (ABCD/NP)

CS 499: Advanced Reading and Research

Letter grade only. Advanced reading and research for CS graduate students. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Montanari, A. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 499P: Advanced Reading and Research

Graded satisfactory/no credit. Advanced reading and research for CS graduate students. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 545: Database and Information Management Seminar

Current research and industrial innovation in database and information systems.
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Leskovec, J. (PI)

CS 546: Seminar on Liberation Technologies (POLISCI 337S)

This one-unit seminar will present speakers relevant in a variety of ways to how various forms of information technology are being used to defend human rights, improve governance, deepen democracy, empower the poor, promote economic development, protect the environment, enhance public health, and pursue a variety of other social goods.
Terms: Aut, Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

CS 547: Human-Computer Interaction Seminar

Weekly speakers. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Bernstein, M. (PI); Heer, J. (PI); Klemmer, S. (PI)

CS 548: Internet and Distributed Systems Seminar

Guest speakers from academia and industry. May be repeated for credit.
Terms: not given this year | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

CS 571: Surgical Robotics Seminar (ME 571)

Surgical robots developed and implemented clinically on varying scales. Seminar goal is to expose students from engineering, medicine, and business to guest lecturers from academia and industry.engineering and clinical aspects connected to design and use of surgical robots, varying in degree of complexity and procedural role. May be repeated for credit.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Barbagli, F. (PI); Okamura, A. (PI)

CS 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
Instructors: Abadi, M. (PI); Aiken, A. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Pea, R. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)

CS 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
Instructors: Abadi, M. (PI); Aiken, A. (PI); Akeley, K. (PI); Altman, R. (PI); Baker, M. (PI); Barbagli, F. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Bradski, G. (PI); Brafman, R. (PI); Cain, G. (PI); Cao, P. (PI); Casado, M. (PI); Cheriton, D. (PI); Cooper, S. (PI); Dally, W. (PI); De-Micheli, G. (PI); Dill, D. (PI); Dwork, C. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fogg, B. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Golub, G. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Heer, J. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Johnson, M. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Klemmer, S. (PI); Koller, D. (PI); Koltun, V. (PI); Konolige, K. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manna, Z. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McCluskey, E. (PI); McKeown, N. (PI); Meng, T. (PI); Mitchell, J. (PI); Motwani, R. (PI); Musen, M. (PI); Nass, C. (PI); Nayak, P. (PI); Ng, A. (PI); Nilsson, N. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Pea, R. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trevisan, L. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Williams, R. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI); Zhao, F. (PI)